Title string | Abstract string | Status string | User string | text string | label int64 | combined_text string | __index_level_0__ int64 |
|---|---|---|---|---|---|---|---|
Introducing Visual Perception Token into Multimodal Large Language Model | To utilize visual information, Multimodal Large Language Model (MLLM) relies
on the perception process of its vision encoder. The completeness and accuracy
of visual perception significantly influence the precision of spatial
reasoning, fine-grained understanding, and other tasks. However, MLLM still
lacks the autonomous capability to control its own visual perception processes,
for example, selectively reviewing specific regions of an image or focusing on
information related to specific object categories. In this work, we propose the
concept of Visual Perception Token, aiming to empower MLLM with a mechanism to
control its visual perception processes. We design two types of Visual
Perception Tokens, termed the Region Selection Token and the Vision Re-Encoding
Token. MLLMs autonomously generate these tokens, just as they generate text,
and use them to trigger additional visual perception actions. The Region
Selection Token explicitly identifies specific regions in an image that require
further perception, while the Vision Re-Encoding Token uses its hidden states
as control signals to guide additional visual perception processes. Extensive
experiments demonstrate the advantages of these tokens in handling spatial
reasoning, improving fine-grained understanding, and other tasks. On average,
the introduction of Visual Perception Tokens improves the performance of a 2B
model by 23.6\%, increasing its score from 0.572 to 0.708, and even outperforms
a 7B parameter model by 13.4\% (from 0.624). Please check out our repo
https://github.com/yu-rp/VisualPerceptionToken | Liked | zrz@andrew.cmu.edu | Introducing Visual Perception Token into Multimodal Large Language Model : To utilize visual information, Multimodal Large Language Model (MLLM) relies
on the perception process of its vision encoder. The completeness and accuracy
of visual perception significantly influence the precision of spatial
reasoning, fine-grained understanding, and other tasks. However, MLLM still
lacks the autonomous capability to control its own visual perception processes,
for example, selectively reviewing specific regions of an image or focusing on
information related to specific object categories. In this work, we propose the
concept of Visual Perception Token, aiming to empower MLLM with a mechanism to
control its visual perception processes. We design two types of Visual
Perception Tokens, termed the Region Selection Token and the Vision Re-Encoding
Token. MLLMs autonomously generate these tokens, just as they generate text,
and use them to trigger additional visual perception actions. The Region
Selection Token explicitly identifies specific regions in an image that require
further perception, while the Vision Re-Encoding Token uses its hidden states
as control signals to guide additional visual perception processes. Extensive
experiments demonstrate the advantages of these tokens in handling spatial
reasoning, improving fine-grained understanding, and other tasks. On average,
the introduction of Visual Perception Tokens improves the performance of a 2B
model by 23.6\%, increasing its score from 0.572 to 0.708, and even outperforms
a 7B parameter model by 13.4\% (from 0.624). Please check out our repo
https://github.com/yu-rp/VisualPerceptionToken | 1 | zrz@andrew.cmu.edu [SEP] Introducing Visual Perception Token into Multimodal Large Language Model : To utilize visual information, Multimodal Large Language Model (MLLM) relies
on the perception process of its vision encoder. The completeness and accuracy
of visual perception significantly influence the precision of spatial
reasoning, fine-grained understanding, and other tasks. However, MLLM still
lacks the autonomous capability to control its own visual perception processes,
for example, selectively reviewing specific regions of an image or focusing on
information related to specific object categories. In this work, we propose the
concept of Visual Perception Token, aiming to empower MLLM with a mechanism to
control its visual perception processes. We design two types of Visual
Perception Tokens, termed the Region Selection Token and the Vision Re-Encoding
Token. MLLMs autonomously generate these tokens, just as they generate text,
and use them to trigger additional visual perception actions. The Region
Selection Token explicitly identifies specific regions in an image that require
further perception, while the Vision Re-Encoding Token uses its hidden states
as control signals to guide additional visual perception processes. Extensive
experiments demonstrate the advantages of these tokens in handling spatial
reasoning, improving fine-grained understanding, and other tasks. On average,
the introduction of Visual Perception Tokens improves the performance of a 2B
model by 23.6\%, increasing its score from 0.572 to 0.708, and even outperforms
a 7B parameter model by 13.4\% (from 0.624). Please check out our repo
https://github.com/yu-rp/VisualPerceptionToken | 291 |
Control the Soft Robot Arm with its Physical Twin | To exploit the compliant capabilities of soft robot arms we require
controller which can exploit their physical capabilities. Teleoperation,
leveraging a human in the loop, is a key step towards achieving more complex
control strategies. Whilst teleoperation is widely used for rigid robots, for
soft robots we require teleoperation methods where the configuration of the
whole body is considered. We propose a method of using an identical 'physical
twin', or demonstrator of the robot. This tendon robot can be back-driven, with
the tendon lengths providing configuration perception, and enabling a direct
mapping of tendon lengths for the execture. We demonstrate how this
teleoperation across the entire configuration of the robot enables complex
interactions with exploit the envrionment, such as squeezing into gaps. We also
show how this method can generalize to robots which are a larger scale that the
physical twin, and how, tuneability of the stiffness properties of the physical
twin simplify its use. | Liked | jechoi@andrew.cmu.edu | Control the Soft Robot Arm with its Physical Twin : To exploit the compliant capabilities of soft robot arms we require
controller which can exploit their physical capabilities. Teleoperation,
leveraging a human in the loop, is a key step towards achieving more complex
control strategies. Whilst teleoperation is widely used for rigid robots, for
soft robots we require teleoperation methods where the configuration of the
whole body is considered. We propose a method of using an identical 'physical
twin', or demonstrator of the robot. This tendon robot can be back-driven, with
the tendon lengths providing configuration perception, and enabling a direct
mapping of tendon lengths for the execture. We demonstrate how this
teleoperation across the entire configuration of the robot enables complex
interactions with exploit the envrionment, such as squeezing into gaps. We also
show how this method can generalize to robots which are a larger scale that the
physical twin, and how, tuneability of the stiffness properties of the physical
twin simplify its use. | 1 | jechoi@andrew.cmu.edu [SEP] Control the Soft Robot Arm with its Physical Twin : To exploit the compliant capabilities of soft robot arms we require
controller which can exploit their physical capabilities. Teleoperation,
leveraging a human in the loop, is a key step towards achieving more complex
control strategies. Whilst teleoperation is widely used for rigid robots, for
soft robots we require teleoperation methods where the configuration of the
whole body is considered. We propose a method of using an identical 'physical
twin', or demonstrator of the robot. This tendon robot can be back-driven, with
the tendon lengths providing configuration perception, and enabling a direct
mapping of tendon lengths for the execture. We demonstrate how this
teleoperation across the entire configuration of the robot enables complex
interactions with exploit the envrionment, such as squeezing into gaps. We also
show how this method can generalize to robots which are a larger scale that the
physical twin, and how, tuneability of the stiffness properties of the physical
twin simplify its use. | 565 |
Four-Arm Collaboration: Two Dual-Arm Robots Work Together to Maneuver Tethered Tools | In this paper, we present a planner for a master dual-arm robot to manipulate
tethered tools with an assistant dual-arm robot's help. The assistant robot
provides assistance to the master robot by manipulating the tool cable and
avoiding collisions. The provided assistance allows the master robot to perform
tool placements on the robot workspace table to regrasp the tool, which would
typically fail since the tool cable tension may change the tool positions. It
also allows the master robot to perform tool handovers, which would normally
cause entanglements or collisions with the cable and the environment without
the assistance. Simulations and real-world experiments are performed to
validate the proposed planner. | Disliked | jechoi@andrew.cmu.edu | Four-Arm Collaboration: Two Dual-Arm Robots Work Together to Maneuver Tethered Tools : In this paper, we present a planner for a master dual-arm robot to manipulate
tethered tools with an assistant dual-arm robot's help. The assistant robot
provides assistance to the master robot by manipulating the tool cable and
avoiding collisions. The provided assistance allows the master robot to perform
tool placements on the robot workspace table to regrasp the tool, which would
typically fail since the tool cable tension may change the tool positions. It
also allows the master robot to perform tool handovers, which would normally
cause entanglements or collisions with the cable and the environment without
the assistance. Simulations and real-world experiments are performed to
validate the proposed planner. | 0 | jechoi@andrew.cmu.edu [SEP] Four-Arm Collaboration: Two Dual-Arm Robots Work Together to Maneuver Tethered Tools : In this paper, we present a planner for a master dual-arm robot to manipulate
tethered tools with an assistant dual-arm robot's help. The assistant robot
provides assistance to the master robot by manipulating the tool cable and
avoiding collisions. The provided assistance allows the master robot to perform
tool placements on the robot workspace table to regrasp the tool, which would
typically fail since the tool cable tension may change the tool positions. It
also allows the master robot to perform tool handovers, which would normally
cause entanglements or collisions with the cable and the environment without
the assistance. Simulations and real-world experiments are performed to
validate the proposed planner. | 395 |
Multi-Arm Robot Task Planning for Fruit Harvesting Using Multi-Agent Reinforcement Learning | The emergence of harvesting robotics offers a promising solution to the issue
of limited agricultural labor resources and the increasing demand for fruits.
Despite notable advancements in the field of harvesting robotics, the
utilization of such technology in orchards is still limited. The key challenge
is to improve operational efficiency. Taking into account inner-arm conflicts,
couplings of DoFs, and dynamic tasks, we propose a task planning strategy for a
harvesting robot with four arms in this paper. The proposed method employs a
Markov game framework to formulate the four-arm robotic harvesting task, which
avoids the computational complexity of solving an NP-hard scheduling problem.
Furthermore, a multi-agent reinforcement learning (MARL) structure with a fully
centralized collaboration protocol is used to train a MARL-based task planning
network. Several simulations and orchard experiments are conducted to validate
the effectiveness of the proposed method for a multi-arm harvesting robot in
comparison with the existing method. | Disliked | jechoi@andrew.cmu.edu | Multi-Arm Robot Task Planning for Fruit Harvesting Using Multi-Agent Reinforcement Learning : The emergence of harvesting robotics offers a promising solution to the issue
of limited agricultural labor resources and the increasing demand for fruits.
Despite notable advancements in the field of harvesting robotics, the
utilization of such technology in orchards is still limited. The key challenge
is to improve operational efficiency. Taking into account inner-arm conflicts,
couplings of DoFs, and dynamic tasks, we propose a task planning strategy for a
harvesting robot with four arms in this paper. The proposed method employs a
Markov game framework to formulate the four-arm robotic harvesting task, which
avoids the computational complexity of solving an NP-hard scheduling problem.
Furthermore, a multi-agent reinforcement learning (MARL) structure with a fully
centralized collaboration protocol is used to train a MARL-based task planning
network. Several simulations and orchard experiments are conducted to validate
the effectiveness of the proposed method for a multi-arm harvesting robot in
comparison with the existing method. | 0 | jechoi@andrew.cmu.edu [SEP] Multi-Arm Robot Task Planning for Fruit Harvesting Using Multi-Agent Reinforcement Learning : The emergence of harvesting robotics offers a promising solution to the issue
of limited agricultural labor resources and the increasing demand for fruits.
Despite notable advancements in the field of harvesting robotics, the
utilization of such technology in orchards is still limited. The key challenge
is to improve operational efficiency. Taking into account inner-arm conflicts,
couplings of DoFs, and dynamic tasks, we propose a task planning strategy for a
harvesting robot with four arms in this paper. The proposed method employs a
Markov game framework to formulate the four-arm robotic harvesting task, which
avoids the computational complexity of solving an NP-hard scheduling problem.
Furthermore, a multi-agent reinforcement learning (MARL) structure with a fully
centralized collaboration protocol is used to train a MARL-based task planning
network. Several simulations and orchard experiments are conducted to validate
the effectiveness of the proposed method for a multi-arm harvesting robot in
comparison with the existing method. | 451 |
Deep Divergence Learning | Classical linear metric learning methods have recently been extended along
two distinct lines: deep metric learning methods for learning embeddings of the
data using neural networks, and Bregman divergence learning approaches for
extending learning Euclidean distances to more general divergence measures such
as divergences over distributions. In this paper, we introduce deep Bregman
divergences, which are based on learning and parameterizing functional Bregman
divergences using neural networks, and which unify and extend these existing
lines of work. We show in particular how deep metric learning formulations,
kernel metric learning, Mahalanobis metric learning, and moment-matching
functions for comparing distributions arise as special cases of these
divergences in the symmetric setting. We then describe a deep learning
framework for learning general functional Bregman divergences, and show in
experiments that this method yields superior performance on benchmark datasets
as compared to existing deep metric learning approaches. We also discuss novel
applications, including a semi-supervised distributional clustering problem,
and a new loss function for unsupervised data generation. | Liked | zrz@andrew.cmu.edu | Deep Divergence Learning : Classical linear metric learning methods have recently been extended along
two distinct lines: deep metric learning methods for learning embeddings of the
data using neural networks, and Bregman divergence learning approaches for
extending learning Euclidean distances to more general divergence measures such
as divergences over distributions. In this paper, we introduce deep Bregman
divergences, which are based on learning and parameterizing functional Bregman
divergences using neural networks, and which unify and extend these existing
lines of work. We show in particular how deep metric learning formulations,
kernel metric learning, Mahalanobis metric learning, and moment-matching
functions for comparing distributions arise as special cases of these
divergences in the symmetric setting. We then describe a deep learning
framework for learning general functional Bregman divergences, and show in
experiments that this method yields superior performance on benchmark datasets
as compared to existing deep metric learning approaches. We also discuss novel
applications, including a semi-supervised distributional clustering problem,
and a new loss function for unsupervised data generation. | 1 | zrz@andrew.cmu.edu [SEP] Deep Divergence Learning : Classical linear metric learning methods have recently been extended along
two distinct lines: deep metric learning methods for learning embeddings of the
data using neural networks, and Bregman divergence learning approaches for
extending learning Euclidean distances to more general divergence measures such
as divergences over distributions. In this paper, we introduce deep Bregman
divergences, which are based on learning and parameterizing functional Bregman
divergences using neural networks, and which unify and extend these existing
lines of work. We show in particular how deep metric learning formulations,
kernel metric learning, Mahalanobis metric learning, and moment-matching
functions for comparing distributions arise as special cases of these
divergences in the symmetric setting. We then describe a deep learning
framework for learning general functional Bregman divergences, and show in
experiments that this method yields superior performance on benchmark datasets
as compared to existing deep metric learning approaches. We also discuss novel
applications, including a semi-supervised distributional clustering problem,
and a new loss function for unsupervised data generation. | 220 |
Real-time Streaming Perception System for Autonomous Driving | Nowadays, plenty of deep learning technologies are being applied to all
aspects of autonomous driving with promising results. Among them, object
detection is the key to improve the ability of an autonomous agent to perceive
its environment so that it can (re)act. However, previous vision-based object
detectors cannot achieve satisfactory performance under real-time driving
scenarios. To remedy this, we present the real-time steaming perception system
in this paper, which is also the 2nd Place solution of Streaming Perception
Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only
track. Unlike traditional object detection challenges, which focus mainly on
the absolute performance, streaming perception task requires achieving a
balance of accuracy and latency, which is crucial for real-time autonomous
driving. We adopt YOLOv5 as our basic framework, data augmentation,
Bag-of-Freebies, and Transformer are adopted to improve streaming object
detection performance with negligible extra inference cost. On the Argoverse-HD
test set, our method achieves 33.2 streaming AP (34.6 streaming AP verified by
the organizer) under the required hardware. Its performance significantly
surpasses the fixed baseline of 13.6 (host team), demonstrating the
potentiality of application. | Liked | zrz@andrew.cmu.edu | Real-time Streaming Perception System for Autonomous Driving : Nowadays, plenty of deep learning technologies are being applied to all
aspects of autonomous driving with promising results. Among them, object
detection is the key to improve the ability of an autonomous agent to perceive
its environment so that it can (re)act. However, previous vision-based object
detectors cannot achieve satisfactory performance under real-time driving
scenarios. To remedy this, we present the real-time steaming perception system
in this paper, which is also the 2nd Place solution of Streaming Perception
Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only
track. Unlike traditional object detection challenges, which focus mainly on
the absolute performance, streaming perception task requires achieving a
balance of accuracy and latency, which is crucial for real-time autonomous
driving. We adopt YOLOv5 as our basic framework, data augmentation,
Bag-of-Freebies, and Transformer are adopted to improve streaming object
detection performance with negligible extra inference cost. On the Argoverse-HD
test set, our method achieves 33.2 streaming AP (34.6 streaming AP verified by
the organizer) under the required hardware. Its performance significantly
surpasses the fixed baseline of 13.6 (host team), demonstrating the
potentiality of application. | 1 | zrz@andrew.cmu.edu [SEP] Real-time Streaming Perception System for Autonomous Driving : Nowadays, plenty of deep learning technologies are being applied to all
aspects of autonomous driving with promising results. Among them, object
detection is the key to improve the ability of an autonomous agent to perceive
its environment so that it can (re)act. However, previous vision-based object
detectors cannot achieve satisfactory performance under real-time driving
scenarios. To remedy this, we present the real-time steaming perception system
in this paper, which is also the 2nd Place solution of Streaming Perception
Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only
track. Unlike traditional object detection challenges, which focus mainly on
the absolute performance, streaming perception task requires achieving a
balance of accuracy and latency, which is crucial for real-time autonomous
driving. We adopt YOLOv5 as our basic framework, data augmentation,
Bag-of-Freebies, and Transformer are adopted to improve streaming object
detection performance with negligible extra inference cost. On the Argoverse-HD
test set, our method achieves 33.2 streaming AP (34.6 streaming AP verified by
the organizer) under the required hardware. Its performance significantly
surpasses the fixed baseline of 13.6 (host team), demonstrating the
potentiality of application. | 302 |
Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception | Autonomous vehicles (AVs) rely on sophisticated perception systems to
interpret their surroundings, a cornerstone for safe navigation and
decision-making. The integration of Large Language Models (LLMs) into AV
perception frameworks offers an innovative approach to address challenges in
dynamic environments, sensor fusion, and contextual reasoning. This paper
presents a novel framework for incorporating LLMs into AV perception, enabling
advanced contextual understanding, seamless sensor integration, and enhanced
decision support. Experimental results demonstrate that LLMs significantly
improve the accuracy and reliability of AV perception systems, paving the way
for safer and more intelligent autonomous driving technologies. By expanding
the scope of perception beyond traditional methods, LLMs contribute to creating
a more adaptive and human-centric driving ecosystem, making autonomous vehicles
more reliable and transparent in their operations. These advancements redefine
the relationship between human drivers and autonomous systems, fostering trust
through enhanced understanding and personalized decision-making. Furthermore,
by integrating memory modules and adaptive learning mechanisms, LLMs introduce
continuous improvement in AV perception, enabling vehicles to evolve with time
and adapt to changing environments and user preferences. | Liked | zrz@andrew.cmu.edu | Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception : Autonomous vehicles (AVs) rely on sophisticated perception systems to
interpret their surroundings, a cornerstone for safe navigation and
decision-making. The integration of Large Language Models (LLMs) into AV
perception frameworks offers an innovative approach to address challenges in
dynamic environments, sensor fusion, and contextual reasoning. This paper
presents a novel framework for incorporating LLMs into AV perception, enabling
advanced contextual understanding, seamless sensor integration, and enhanced
decision support. Experimental results demonstrate that LLMs significantly
improve the accuracy and reliability of AV perception systems, paving the way
for safer and more intelligent autonomous driving technologies. By expanding
the scope of perception beyond traditional methods, LLMs contribute to creating
a more adaptive and human-centric driving ecosystem, making autonomous vehicles
more reliable and transparent in their operations. These advancements redefine
the relationship between human drivers and autonomous systems, fostering trust
through enhanced understanding and personalized decision-making. Furthermore,
by integrating memory modules and adaptive learning mechanisms, LLMs introduce
continuous improvement in AV perception, enabling vehicles to evolve with time
and adapt to changing environments and user preferences. | 1 | zrz@andrew.cmu.edu [SEP] Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception : Autonomous vehicles (AVs) rely on sophisticated perception systems to
interpret their surroundings, a cornerstone for safe navigation and
decision-making. The integration of Large Language Models (LLMs) into AV
perception frameworks offers an innovative approach to address challenges in
dynamic environments, sensor fusion, and contextual reasoning. This paper
presents a novel framework for incorporating LLMs into AV perception, enabling
advanced contextual understanding, seamless sensor integration, and enhanced
decision support. Experimental results demonstrate that LLMs significantly
improve the accuracy and reliability of AV perception systems, paving the way
for safer and more intelligent autonomous driving technologies. By expanding
the scope of perception beyond traditional methods, LLMs contribute to creating
a more adaptive and human-centric driving ecosystem, making autonomous vehicles
more reliable and transparent in their operations. These advancements redefine
the relationship between human drivers and autonomous systems, fostering trust
through enhanced understanding and personalized decision-making. Furthermore,
by integrating memory modules and adaptive learning mechanisms, LLMs introduce
continuous improvement in AV perception, enabling vehicles to evolve with time
and adapt to changing environments and user preferences. | 276 |
A Shared Autonomy Reconfigurable Control Framework for Telemanipulation of Multi-arm Systems | Teleoperation is a widely adopted strategy to control robotic manipulators
executing complex tasks that require highly dexterous movements and critical
high-level intelligence. Classical teleoperation schemes are based on either
joystick control, or on more intuitive interfaces which map directly the user
arm motions into one robot arm's motions. These approaches have limits when the
execution of a given task requires reconfigurable multiple robotic arm systems.
Indeed, the simultaneous teleoperation of two or more robot arms could extend
the workspace of the manipulation cell, or increase its total payload, or
afford other advantages. In different phases of a reconfigurable multi-arm
system, each robot could act as an independent arm, or as one of a pair of
cooperating arms, or as one of the fingers of a virtual, large robot hand. This
manuscript proposes a novel telemanipulation framework that enables both the
individual and combined control of any number of robotic arms. Thanks to the
designed control architecture, the human operator can intuitively choose the
proposed control modalities and the manipulators that make the task convenient
to execute through the user interface. Moreover, through the tele-impedance
paradigm, the system can address complex tasks that require physical
interaction by letting the robot mimic the arm impedance and position
references of the human operator. The proposed framework is validated with 8
subjects controlling 4 Franka Emika Panda robots with 7-DoFs to execute a
telemanipulation task. Qualitative results of the experiments show us the
promising applicability of our framework. | Liked | jechoi@andrew.cmu.edu | A Shared Autonomy Reconfigurable Control Framework for Telemanipulation of Multi-arm Systems : Teleoperation is a widely adopted strategy to control robotic manipulators
executing complex tasks that require highly dexterous movements and critical
high-level intelligence. Classical teleoperation schemes are based on either
joystick control, or on more intuitive interfaces which map directly the user
arm motions into one robot arm's motions. These approaches have limits when the
execution of a given task requires reconfigurable multiple robotic arm systems.
Indeed, the simultaneous teleoperation of two or more robot arms could extend
the workspace of the manipulation cell, or increase its total payload, or
afford other advantages. In different phases of a reconfigurable multi-arm
system, each robot could act as an independent arm, or as one of a pair of
cooperating arms, or as one of the fingers of a virtual, large robot hand. This
manuscript proposes a novel telemanipulation framework that enables both the
individual and combined control of any number of robotic arms. Thanks to the
designed control architecture, the human operator can intuitively choose the
proposed control modalities and the manipulators that make the task convenient
to execute through the user interface. Moreover, through the tele-impedance
paradigm, the system can address complex tasks that require physical
interaction by letting the robot mimic the arm impedance and position
references of the human operator. The proposed framework is validated with 8
subjects controlling 4 Franka Emika Panda robots with 7-DoFs to execute a
telemanipulation task. Qualitative results of the experiments show us the
promising applicability of our framework. | 1 | jechoi@andrew.cmu.edu [SEP] A Shared Autonomy Reconfigurable Control Framework for Telemanipulation of Multi-arm Systems : Teleoperation is a widely adopted strategy to control robotic manipulators
executing complex tasks that require highly dexterous movements and critical
high-level intelligence. Classical teleoperation schemes are based on either
joystick control, or on more intuitive interfaces which map directly the user
arm motions into one robot arm's motions. These approaches have limits when the
execution of a given task requires reconfigurable multiple robotic arm systems.
Indeed, the simultaneous teleoperation of two or more robot arms could extend
the workspace of the manipulation cell, or increase its total payload, or
afford other advantages. In different phases of a reconfigurable multi-arm
system, each robot could act as an independent arm, or as one of a pair of
cooperating arms, or as one of the fingers of a virtual, large robot hand. This
manuscript proposes a novel telemanipulation framework that enables both the
individual and combined control of any number of robotic arms. Thanks to the
designed control architecture, the human operator can intuitively choose the
proposed control modalities and the manipulators that make the task convenient
to execute through the user interface. Moreover, through the tele-impedance
paradigm, the system can address complex tasks that require physical
interaction by letting the robot mimic the arm impedance and position
references of the human operator. The proposed framework is validated with 8
subjects controlling 4 Franka Emika Panda robots with 7-DoFs to execute a
telemanipulation task. Qualitative results of the experiments show us the
promising applicability of our framework. | 436 |
Deep Online Learning with Stochastic Constraints | Deep learning models are considered to be state-of-the-art in many offline
machine learning tasks. However, many of the techniques developed are not
suitable for online learning tasks. The problem of using deep learning models
with sequential data becomes even harder when several loss functions need to be
considered simultaneously, as in many real-world applications. In this paper,
we, therefore, propose a novel online deep learning training procedure which
can be used regardless of the neural network's architecture, aiming to deal
with the multiple objectives case. We demonstrate and show the effectiveness of
our algorithm on the Neyman-Pearson classification problem on several benchmark
datasets. | Disliked | zrz@andrew.cmu.edu | Deep Online Learning with Stochastic Constraints : Deep learning models are considered to be state-of-the-art in many offline
machine learning tasks. However, many of the techniques developed are not
suitable for online learning tasks. The problem of using deep learning models
with sequential data becomes even harder when several loss functions need to be
considered simultaneously, as in many real-world applications. In this paper,
we, therefore, propose a novel online deep learning training procedure which
can be used regardless of the neural network's architecture, aiming to deal
with the multiple objectives case. We demonstrate and show the effectiveness of
our algorithm on the Neyman-Pearson classification problem on several benchmark
datasets. | 0 | zrz@andrew.cmu.edu [SEP] Deep Online Learning with Stochastic Constraints : Deep learning models are considered to be state-of-the-art in many offline
machine learning tasks. However, many of the techniques developed are not
suitable for online learning tasks. The problem of using deep learning models
with sequential data becomes even harder when several loss functions need to be
considered simultaneously, as in many real-world applications. In this paper,
we, therefore, propose a novel online deep learning training procedure which
can be used regardless of the neural network's architecture, aiming to deal
with the multiple objectives case. We demonstrate and show the effectiveness of
our algorithm on the Neyman-Pearson classification problem on several benchmark
datasets. | 254 |
Model Complexity of Deep Learning: A Survey | Model complexity is a fundamental problem in deep learning. In this paper we
conduct a systematic overview of the latest studies on model complexity in deep
learning. Model complexity of deep learning can be categorized into expressive
capacity and effective model complexity. We review the existing studies on
those two categories along four important factors, including model framework,
model size, optimization process and data complexity. We also discuss the
applications of deep learning model complexity including understanding model
generalization, model optimization, and model selection and design. We conclude
by proposing several interesting future directions. | Disliked | zrz@andrew.cmu.edu | Model Complexity of Deep Learning: A Survey : Model complexity is a fundamental problem in deep learning. In this paper we
conduct a systematic overview of the latest studies on model complexity in deep
learning. Model complexity of deep learning can be categorized into expressive
capacity and effective model complexity. We review the existing studies on
those two categories along four important factors, including model framework,
model size, optimization process and data complexity. We also discuss the
applications of deep learning model complexity including understanding model
generalization, model optimization, and model selection and design. We conclude
by proposing several interesting future directions. | 0 | zrz@andrew.cmu.edu [SEP] Model Complexity of Deep Learning: A Survey : Model complexity is a fundamental problem in deep learning. In this paper we
conduct a systematic overview of the latest studies on model complexity in deep
learning. Model complexity of deep learning can be categorized into expressive
capacity and effective model complexity. We review the existing studies on
those two categories along four important factors, including model framework,
model size, optimization process and data complexity. We also discuss the
applications of deep learning model complexity including understanding model
generalization, model optimization, and model selection and design. We conclude
by proposing several interesting future directions. | 243 |
SpaceOctopus: An Octopus-inspired Motion Planning Framework for Multi-arm Space Robot | Space robots have played a critical role in autonomous maintenance and space
junk removal. Multi-arm space robots can efficiently complete the target
capture and base reorientation tasks due to their flexibility and the
collaborative capabilities between the arms. However, the complex coupling
properties arising from both the multiple arms and the free-floating base
present challenges to the motion planning problems of multi-arm space robots.
We observe that the octopus elegantly achieves similar goals when grabbing prey
and escaping from danger. Inspired by the distributed control of octopuses'
limbs, we develop a multi-level decentralized motion planning framework to
manage the movement of different arms of space robots. This motion planning
framework integrates naturally with the multi-agent reinforcement learning
(MARL) paradigm. The results indicate that our method outperforms the previous
method (centralized training). Leveraging the flexibility of the decentralized
framework, we reassemble policies trained for different tasks, enabling the
space robot to complete trajectory planning tasks while adjusting the base
attitude without further learning. Furthermore, our experiments confirm the
superior robustness of our method in the face of external disturbances,
changing base masses, and even the failure of one arm. | Disliked | jechoi@andrew.cmu.edu | SpaceOctopus: An Octopus-inspired Motion Planning Framework for Multi-arm Space Robot : Space robots have played a critical role in autonomous maintenance and space
junk removal. Multi-arm space robots can efficiently complete the target
capture and base reorientation tasks due to their flexibility and the
collaborative capabilities between the arms. However, the complex coupling
properties arising from both the multiple arms and the free-floating base
present challenges to the motion planning problems of multi-arm space robots.
We observe that the octopus elegantly achieves similar goals when grabbing prey
and escaping from danger. Inspired by the distributed control of octopuses'
limbs, we develop a multi-level decentralized motion planning framework to
manage the movement of different arms of space robots. This motion planning
framework integrates naturally with the multi-agent reinforcement learning
(MARL) paradigm. The results indicate that our method outperforms the previous
method (centralized training). Leveraging the flexibility of the decentralized
framework, we reassemble policies trained for different tasks, enabling the
space robot to complete trajectory planning tasks while adjusting the base
attitude without further learning. Furthermore, our experiments confirm the
superior robustness of our method in the face of external disturbances,
changing base masses, and even the failure of one arm. | 0 | jechoi@andrew.cmu.edu [SEP] SpaceOctopus: An Octopus-inspired Motion Planning Framework for Multi-arm Space Robot : Space robots have played a critical role in autonomous maintenance and space
junk removal. Multi-arm space robots can efficiently complete the target
capture and base reorientation tasks due to their flexibility and the
collaborative capabilities between the arms. However, the complex coupling
properties arising from both the multiple arms and the free-floating base
present challenges to the motion planning problems of multi-arm space robots.
We observe that the octopus elegantly achieves similar goals when grabbing prey
and escaping from danger. Inspired by the distributed control of octopuses'
limbs, we develop a multi-level decentralized motion planning framework to
manage the movement of different arms of space robots. This motion planning
framework integrates naturally with the multi-agent reinforcement learning
(MARL) paradigm. The results indicate that our method outperforms the previous
method (centralized training). Leveraging the flexibility of the decentralized
framework, we reassemble policies trained for different tasks, enabling the
space robot to complete trajectory planning tasks while adjusting the base
attitude without further learning. Furthermore, our experiments confirm the
superior robustness of our method in the face of external disturbances,
changing base masses, and even the failure of one arm. | 408 |
Assisting MoCap-Based Teleoperation of Robot Arm using Augmented Reality Visualisations | Teleoperating a robot arm involves the human operator positioning the robot's
end-effector or programming each joint. Whereas humans can control their own
arms easily by integrating visual and proprioceptive feedback, it is
challenging to control an external robot arm in the same way, due to its
inconsistent orientation and appearance. We explore teleoperating a robot arm
through motion-capture (MoCap) of the human operator's arm with the assistance
of augmented reality (AR) visualisations. We investigate how AR helps
teleoperation by visualising a virtual reference of the human arm alongside the
robot arm to help users understand the movement mapping. We found that the AR
overlay of a humanoid arm on the robot in the same orientation helped users
learn the control. We discuss findings and future work on MoCap-based robot
teleoperation. | Liked | jechoi@andrew.cmu.edu | Assisting MoCap-Based Teleoperation of Robot Arm using Augmented Reality Visualisations : Teleoperating a robot arm involves the human operator positioning the robot's
end-effector or programming each joint. Whereas humans can control their own
arms easily by integrating visual and proprioceptive feedback, it is
challenging to control an external robot arm in the same way, due to its
inconsistent orientation and appearance. We explore teleoperating a robot arm
through motion-capture (MoCap) of the human operator's arm with the assistance
of augmented reality (AR) visualisations. We investigate how AR helps
teleoperation by visualising a virtual reference of the human arm alongside the
robot arm to help users understand the movement mapping. We found that the AR
overlay of a humanoid arm on the robot in the same orientation helped users
learn the control. We discuss findings and future work on MoCap-based robot
teleoperation. | 1 | jechoi@andrew.cmu.edu [SEP] Assisting MoCap-Based Teleoperation of Robot Arm using Augmented Reality Visualisations : Teleoperating a robot arm involves the human operator positioning the robot's
end-effector or programming each joint. Whereas humans can control their own
arms easily by integrating visual and proprioceptive feedback, it is
challenging to control an external robot arm in the same way, due to its
inconsistent orientation and appearance. We explore teleoperating a robot arm
through motion-capture (MoCap) of the human operator's arm with the assistance
of augmented reality (AR) visualisations. We investigate how AR helps
teleoperation by visualising a virtual reference of the human arm alongside the
robot arm to help users understand the movement mapping. We found that the AR
overlay of a humanoid arm on the robot in the same orientation helped users
learn the control. We discuss findings and future work on MoCap-based robot
teleoperation. | 12 |
The Tribes of Machine Learning and the Realm of Computer Architecture | Machine learning techniques have influenced the field of computer
architecture like many other fields. This paper studies how the fundamental
machine learning techniques can be applied towards computer architecture
problems. We also provide a detailed survey of computer architecture research
that employs different machine learning methods. Finally, we present some
future opportunities and the outstanding challenges that need to be overcome to
exploit full potential of machine learning for computer architecture. | Disliked | zrz@andrew.cmu.edu | The Tribes of Machine Learning and the Realm of Computer Architecture : Machine learning techniques have influenced the field of computer
architecture like many other fields. This paper studies how the fundamental
machine learning techniques can be applied towards computer architecture
problems. We also provide a detailed survey of computer architecture research
that employs different machine learning methods. Finally, we present some
future opportunities and the outstanding challenges that need to be overcome to
exploit full potential of machine learning for computer architecture. | 0 | zrz@andrew.cmu.edu [SEP] The Tribes of Machine Learning and the Realm of Computer Architecture : Machine learning techniques have influenced the field of computer
architecture like many other fields. This paper studies how the fundamental
machine learning techniques can be applied towards computer architecture
problems. We also provide a detailed survey of computer architecture research
that employs different machine learning methods. Finally, we present some
future opportunities and the outstanding challenges that need to be overcome to
exploit full potential of machine learning for computer architecture. | 33 |
What Really is Deep Learning Doing? | Deep learning has achieved a great success in many areas, from computer
vision to natural language processing, to game playing, and much more. Yet,
what deep learning is really doing is still an open question. There are a lot
of works in this direction. For example, [5] tried to explain deep learning by
group renormalization, and [6] tried to explain deep learning from the view of
functional approximation. In order to address this very crucial question, here
we see deep learning from perspective of mechanical learning and learning
machine (see [1], [2]). From this particular angle, we can see deep learning
much better and answer with confidence: What deep learning is really doing? why
it works well, how it works, and how much data is necessary for learning. We
also will discuss advantages and disadvantages of deep learning at the end of
this work. | Liked | zrz@andrew.cmu.edu | What Really is Deep Learning Doing? : Deep learning has achieved a great success in many areas, from computer
vision to natural language processing, to game playing, and much more. Yet,
what deep learning is really doing is still an open question. There are a lot
of works in this direction. For example, [5] tried to explain deep learning by
group renormalization, and [6] tried to explain deep learning from the view of
functional approximation. In order to address this very crucial question, here
we see deep learning from perspective of mechanical learning and learning
machine (see [1], [2]). From this particular angle, we can see deep learning
much better and answer with confidence: What deep learning is really doing? why
it works well, how it works, and how much data is necessary for learning. We
also will discuss advantages and disadvantages of deep learning at the end of
this work. | 1 | zrz@andrew.cmu.edu [SEP] What Really is Deep Learning Doing? : Deep learning has achieved a great success in many areas, from computer
vision to natural language processing, to game playing, and much more. Yet,
what deep learning is really doing is still an open question. There are a lot
of works in this direction. For example, [5] tried to explain deep learning by
group renormalization, and [6] tried to explain deep learning from the view of
functional approximation. In order to address this very crucial question, here
we see deep learning from perspective of mechanical learning and learning
machine (see [1], [2]). From this particular angle, we can see deep learning
much better and answer with confidence: What deep learning is really doing? why
it works well, how it works, and how much data is necessary for learning. We
also will discuss advantages and disadvantages of deep learning at the end of
this work. | 175 |
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning | Contrastive vision-language models (e.g. CLIP) are typically created by
updating all the parameters of a vision model and language model through
contrastive training. Can such models be created by a small number of parameter
updates to an already-trained language model and vision model? The literature
describes techniques that can create vision-language models by updating a small
number of parameters in a language model, but these require already aligned
visual representations and are non-contrastive, hence unusable for
latency-sensitive applications such as neural search. We explore the
feasibility and benefits of parameter-efficient contrastive vision-language
alignment through transfer learning: creating a model such as CLIP by minimally
updating an already-trained vision and language model. We find that a minimal
set of parameter updates ($<$7%) can achieve the same performance as full-model
training, and updating specific components ($<$1% of parameters) can match 75%
of full-model training. We describe a series of experiments: we show that
existing knowledge is conserved more strongly in parameter-efficient training
and that parameter-efficient scaling scales with model and dataset size. Where
paired-image text data is scarce but strong multilingual language models exist
(e.g. low resource languages), parameter-efficient training is even preferable
to full-model training. Given a fixed compute budget, parameter-efficient
training allows training larger models on the same hardware, achieving
equivalent performance in less time. Parameter-efficient training hence
constitutes an energy-efficient and effective training strategy for contrastive
vision-language models that may be preferable to the full-model training
paradigm for common use cases. Code and weights at
https://github.com/codezakh/LilT. | Liked | zrz@andrew.cmu.edu | Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning : Contrastive vision-language models (e.g. CLIP) are typically created by
updating all the parameters of a vision model and language model through
contrastive training. Can such models be created by a small number of parameter
updates to an already-trained language model and vision model? The literature
describes techniques that can create vision-language models by updating a small
number of parameters in a language model, but these require already aligned
visual representations and are non-contrastive, hence unusable for
latency-sensitive applications such as neural search. We explore the
feasibility and benefits of parameter-efficient contrastive vision-language
alignment through transfer learning: creating a model such as CLIP by minimally
updating an already-trained vision and language model. We find that a minimal
set of parameter updates ($<$7%) can achieve the same performance as full-model
training, and updating specific components ($<$1% of parameters) can match 75%
of full-model training. We describe a series of experiments: we show that
existing knowledge is conserved more strongly in parameter-efficient training
and that parameter-efficient scaling scales with model and dataset size. Where
paired-image text data is scarce but strong multilingual language models exist
(e.g. low resource languages), parameter-efficient training is even preferable
to full-model training. Given a fixed compute budget, parameter-efficient
training allows training larger models on the same hardware, achieving
equivalent performance in less time. Parameter-efficient training hence
constitutes an energy-efficient and effective training strategy for contrastive
vision-language models that may be preferable to the full-model training
paradigm for common use cases. Code and weights at
https://github.com/codezakh/LilT. | 1 | zrz@andrew.cmu.edu [SEP] Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning : Contrastive vision-language models (e.g. CLIP) are typically created by
updating all the parameters of a vision model and language model through
contrastive training. Can such models be created by a small number of parameter
updates to an already-trained language model and vision model? The literature
describes techniques that can create vision-language models by updating a small
number of parameters in a language model, but these require already aligned
visual representations and are non-contrastive, hence unusable for
latency-sensitive applications such as neural search. We explore the
feasibility and benefits of parameter-efficient contrastive vision-language
alignment through transfer learning: creating a model such as CLIP by minimally
updating an already-trained vision and language model. We find that a minimal
set of parameter updates ($<$7%) can achieve the same performance as full-model
training, and updating specific components ($<$1% of parameters) can match 75%
of full-model training. We describe a series of experiments: we show that
existing knowledge is conserved more strongly in parameter-efficient training
and that parameter-efficient scaling scales with model and dataset size. Where
paired-image text data is scarce but strong multilingual language models exist
(e.g. low resource languages), parameter-efficient training is even preferable
to full-model training. Given a fixed compute budget, parameter-efficient
training allows training larger models on the same hardware, achieving
equivalent performance in less time. Parameter-efficient training hence
constitutes an energy-efficient and effective training strategy for contrastive
vision-language models that may be preferable to the full-model training
paradigm for common use cases. Code and weights at
https://github.com/codezakh/LilT. | 377 |
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL | We propose a vision-based reinforcement learning (RL) approach for
closed-loop trajectory generation in an arm reaching problem. Arm trajectory
generation is a fundamental robotics problem which entails finding
collision-free paths to move the robot's body (e.g. arm) in order to satisfy a
goal (e.g. place end-effector at a point).
While classical methods typically require the model of the environment to
solve a planning, search or optimization problem, learning-based approaches
hold the promise of directly mapping from observations to robot actions.
However, learning a collision-avoidance policy using RL remains a challenge
for various reasons, including, but not limited to, partial observability, poor
exploration, low sample efficiency, and learning instabilities.
To address these challenges, we present a residual-RL method that leverages a
greedy goal-reaching RL policy as the base to improve exploration, and the base
policy is augmented with residual state-action values and residual actions
learned from images to avoid obstacles. Further more, we introduce novel
learning objectives and techniques to improve 3D understanding from multiple
image views and sample efficiency of our algorithm.
Compared to RL baselines, our method achieves superior performance in terms
of success rate. | Liked | jechoi@andrew.cmu.edu | CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL : We propose a vision-based reinforcement learning (RL) approach for
closed-loop trajectory generation in an arm reaching problem. Arm trajectory
generation is a fundamental robotics problem which entails finding
collision-free paths to move the robot's body (e.g. arm) in order to satisfy a
goal (e.g. place end-effector at a point).
While classical methods typically require the model of the environment to
solve a planning, search or optimization problem, learning-based approaches
hold the promise of directly mapping from observations to robot actions.
However, learning a collision-avoidance policy using RL remains a challenge
for various reasons, including, but not limited to, partial observability, poor
exploration, low sample efficiency, and learning instabilities.
To address these challenges, we present a residual-RL method that leverages a
greedy goal-reaching RL policy as the base to improve exploration, and the base
policy is augmented with residual state-action values and residual actions
learned from images to avoid obstacles. Further more, we introduce novel
learning objectives and techniques to improve 3D understanding from multiple
image views and sample efficiency of our algorithm.
Compared to RL baselines, our method achieves superior performance in terms
of success rate. | 1 | jechoi@andrew.cmu.edu [SEP] CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL : We propose a vision-based reinforcement learning (RL) approach for
closed-loop trajectory generation in an arm reaching problem. Arm trajectory
generation is a fundamental robotics problem which entails finding
collision-free paths to move the robot's body (e.g. arm) in order to satisfy a
goal (e.g. place end-effector at a point).
While classical methods typically require the model of the environment to
solve a planning, search or optimization problem, learning-based approaches
hold the promise of directly mapping from observations to robot actions.
However, learning a collision-avoidance policy using RL remains a challenge
for various reasons, including, but not limited to, partial observability, poor
exploration, low sample efficiency, and learning instabilities.
To address these challenges, we present a residual-RL method that leverages a
greedy goal-reaching RL policy as the base to improve exploration, and the base
policy is augmented with residual state-action values and residual actions
learned from images to avoid obstacles. Further more, we introduce novel
learning objectives and techniques to improve 3D understanding from multiple
image views and sample efficiency of our algorithm.
Compared to RL baselines, our method achieves superior performance in terms
of success rate. | 568 |
Some Requests for Machine Learning Research from the East African Tech Scene | Based on 46 in-depth interviews with scientists, engineers, and CEOs, this
document presents a list of concrete machine research problems, progress on
which would directly benefit tech ventures in East Africa. | Disliked | zrz@andrew.cmu.edu | Some Requests for Machine Learning Research from the East African Tech Scene : Based on 46 in-depth interviews with scientists, engineers, and CEOs, this
document presents a list of concrete machine research problems, progress on
which would directly benefit tech ventures in East Africa. | 0 | zrz@andrew.cmu.edu [SEP] Some Requests for Machine Learning Research from the East African Tech Scene : Based on 46 in-depth interviews with scientists, engineers, and CEOs, this
document presents a list of concrete machine research problems, progress on
which would directly benefit tech ventures in East Africa. | 99 |
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches | Despite the recent success of deep transfer learning approaches in NLP, there
is a lack of quantitative studies demonstrating the gains these models offer in
low-shot text classification tasks over existing paradigms. Deep transfer
learning approaches such as BERT and ULMFiT demonstrate that they can beat
state-of-the-art results on larger datasets, however when one has only 100-1000
labelled examples per class, the choice of approach is less clear, with
classical machine learning and deep transfer learning representing valid
options. This paper compares the current best transfer learning approach with
top classical machine learning approaches on a trinary sentiment classification
task to assess the best paradigm. We find that BERT, representing the best of
deep transfer learning, is the best performing approach, outperforming top
classical machine learning algorithms by 9.7% on average when trained with 100
examples per class, narrowing to 1.8% at 1000 labels per class. We also show
the robustness of deep transfer learning in moving across domains, where the
maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross
domain, compared to classical machine learning which loses up to 20.6%. | Disliked | zrz@andrew.cmu.edu | Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches : Despite the recent success of deep transfer learning approaches in NLP, there
is a lack of quantitative studies demonstrating the gains these models offer in
low-shot text classification tasks over existing paradigms. Deep transfer
learning approaches such as BERT and ULMFiT demonstrate that they can beat
state-of-the-art results on larger datasets, however when one has only 100-1000
labelled examples per class, the choice of approach is less clear, with
classical machine learning and deep transfer learning representing valid
options. This paper compares the current best transfer learning approach with
top classical machine learning approaches on a trinary sentiment classification
task to assess the best paradigm. We find that BERT, representing the best of
deep transfer learning, is the best performing approach, outperforming top
classical machine learning algorithms by 9.7% on average when trained with 100
examples per class, narrowing to 1.8% at 1000 labels per class. We also show
the robustness of deep transfer learning in moving across domains, where the
maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross
domain, compared to classical machine learning which loses up to 20.6%. | 0 | zrz@andrew.cmu.edu [SEP] Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches : Despite the recent success of deep transfer learning approaches in NLP, there
is a lack of quantitative studies demonstrating the gains these models offer in
low-shot text classification tasks over existing paradigms. Deep transfer
learning approaches such as BERT and ULMFiT demonstrate that they can beat
state-of-the-art results on larger datasets, however when one has only 100-1000
labelled examples per class, the choice of approach is less clear, with
classical machine learning and deep transfer learning representing valid
options. This paper compares the current best transfer learning approach with
top classical machine learning approaches on a trinary sentiment classification
task to assess the best paradigm. We find that BERT, representing the best of
deep transfer learning, is the best performing approach, outperforming top
classical machine learning algorithms by 9.7% on average when trained with 100
examples per class, narrowing to 1.8% at 1000 labels per class. We also show
the robustness of deep transfer learning in moving across domains, where the
maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross
domain, compared to classical machine learning which loses up to 20.6%. | 143 |
Octopus-Swimming-Like Robot with Soft Asymmetric Arms | Underwater vehicles have seen significant development over the past seventy
years. However, bio-inspired propulsion robots are still in their early stages
and require greater interdisciplinary collaboration between biologists and
roboticists. The octopus, one of the most intelligent marine animals, exhibits
remarkable abilities such as camouflaging, exploring, and hunting while
swimming with its arms. Although bio-inspired robotics researchers have aimed
to replicate these abilities, the complexity of designing an eight-arm bionic
swimming platform has posed challenges from the beginning. In this work, we
propose a novel bionic robot swimming platform that combines asymmetric passive
morphing arms with an umbrella-like quick-return mechanism. Using only two
simple constant-speed motors, this design achieves efficient swimming by
replicating octopus-like arm movements and stroke time ratios. The robot
reached a peak speed of 314 mm/s during its second power stroke. This design
reduces the complexity of traditional octopus-like swimming robot actuation
systems while maintaining good swimming performance. It offers a more
achievable and efficient platform for biologists and roboticists conducting
more profound octopus-inspired robotic and biological studies. | Disliked | jechoi@andrew.cmu.edu | Octopus-Swimming-Like Robot with Soft Asymmetric Arms : Underwater vehicles have seen significant development over the past seventy
years. However, bio-inspired propulsion robots are still in their early stages
and require greater interdisciplinary collaboration between biologists and
roboticists. The octopus, one of the most intelligent marine animals, exhibits
remarkable abilities such as camouflaging, exploring, and hunting while
swimming with its arms. Although bio-inspired robotics researchers have aimed
to replicate these abilities, the complexity of designing an eight-arm bionic
swimming platform has posed challenges from the beginning. In this work, we
propose a novel bionic robot swimming platform that combines asymmetric passive
morphing arms with an umbrella-like quick-return mechanism. Using only two
simple constant-speed motors, this design achieves efficient swimming by
replicating octopus-like arm movements and stroke time ratios. The robot
reached a peak speed of 314 mm/s during its second power stroke. This design
reduces the complexity of traditional octopus-like swimming robot actuation
systems while maintaining good swimming performance. It offers a more
achievable and efficient platform for biologists and roboticists conducting
more profound octopus-inspired robotic and biological studies. | 0 | jechoi@andrew.cmu.edu [SEP] Octopus-Swimming-Like Robot with Soft Asymmetric Arms : Underwater vehicles have seen significant development over the past seventy
years. However, bio-inspired propulsion robots are still in their early stages
and require greater interdisciplinary collaboration between biologists and
roboticists. The octopus, one of the most intelligent marine animals, exhibits
remarkable abilities such as camouflaging, exploring, and hunting while
swimming with its arms. Although bio-inspired robotics researchers have aimed
to replicate these abilities, the complexity of designing an eight-arm bionic
swimming platform has posed challenges from the beginning. In this work, we
propose a novel bionic robot swimming platform that combines asymmetric passive
morphing arms with an umbrella-like quick-return mechanism. Using only two
simple constant-speed motors, this design achieves efficient swimming by
replicating octopus-like arm movements and stroke time ratios. The robot
reached a peak speed of 314 mm/s during its second power stroke. This design
reduces the complexity of traditional octopus-like swimming robot actuation
systems while maintaining good swimming performance. It offers a more
achievable and efficient platform for biologists and roboticists conducting
more profound octopus-inspired robotic and biological studies. | 421 |
Why & When Deep Learning Works: Looking Inside Deep Learnings | The Intel Collaborative Research Institute for Computational Intelligence
(ICRI-CI) has been heavily supporting Machine Learning and Deep Learning
research from its foundation in 2012. We have asked six leading ICRI-CI Deep
Learning researchers to address the challenge of "Why & When Deep Learning
works", with the goal of looking inside Deep Learning, providing insights on
how deep networks function, and uncovering key observations on their
expressiveness, limitations, and potential. The output of this challenge
resulted in five papers that address different facets of deep learning. These
different facets include a high-level understating of why and when deep
networks work (and do not work), the impact of geometry on the expressiveness
of deep networks, and making deep networks interpretable. | Disliked | zrz@andrew.cmu.edu | Why & When Deep Learning Works: Looking Inside Deep Learnings : The Intel Collaborative Research Institute for Computational Intelligence
(ICRI-CI) has been heavily supporting Machine Learning and Deep Learning
research from its foundation in 2012. We have asked six leading ICRI-CI Deep
Learning researchers to address the challenge of "Why & When Deep Learning
works", with the goal of looking inside Deep Learning, providing insights on
how deep networks function, and uncovering key observations on their
expressiveness, limitations, and potential. The output of this challenge
resulted in five papers that address different facets of deep learning. These
different facets include a high-level understating of why and when deep
networks work (and do not work), the impact of geometry on the expressiveness
of deep networks, and making deep networks interpretable. | 0 | zrz@andrew.cmu.edu [SEP] Why & When Deep Learning Works: Looking Inside Deep Learnings : The Intel Collaborative Research Institute for Computational Intelligence
(ICRI-CI) has been heavily supporting Machine Learning and Deep Learning
research from its foundation in 2012. We have asked six leading ICRI-CI Deep
Learning researchers to address the challenge of "Why & When Deep Learning
works", with the goal of looking inside Deep Learning, providing insights on
how deep networks function, and uncovering key observations on their
expressiveness, limitations, and potential. The output of this challenge
resulted in five papers that address different facets of deep learning. These
different facets include a high-level understating of why and when deep
networks work (and do not work), the impact of geometry on the expressiveness
of deep networks, and making deep networks interpretable. | 161 |
Language Features Matter: Effective Language Representations for Vision-Language Tasks | Shouldn't language and vision features be treated equally in vision-language
(VL) tasks? Many VL approaches treat the language component as an afterthought,
using simple language models that are either built upon fixed word embeddings
trained on text-only data or are learned from scratch. We believe that language
features deserve more attention, and conduct experiments which compare
different word embeddings, language models, and embedding augmentation steps on
five common VL tasks: image-sentence retrieval, image captioning, visual
question answering, phrase grounding, and text-to-clip retrieval. Our
experiments provide some striking results; an average embedding language model
outperforms an LSTM on retrieval-style tasks; state-of-the-art representations
such as BERT perform relatively poorly on vision-language tasks. From this
comprehensive set of experiments we propose a set of best practices for
incorporating the language component of VL tasks. To further elevate language
features, we also show that knowledge in vision-language problems can be
transferred across tasks to gain performance with multi-task training. This
multi-task training is applied to a new Graph Oriented Vision-Language
Embedding (GrOVLE), which we adapt from Word2Vec using WordNet and an original
visual-language graph built from Visual Genome, providing a ready-to-use
vision-language embedding: http://ai.bu.edu/grovle. | Liked | zrz@andrew.cmu.edu | Language Features Matter: Effective Language Representations for Vision-Language Tasks : Shouldn't language and vision features be treated equally in vision-language
(VL) tasks? Many VL approaches treat the language component as an afterthought,
using simple language models that are either built upon fixed word embeddings
trained on text-only data or are learned from scratch. We believe that language
features deserve more attention, and conduct experiments which compare
different word embeddings, language models, and embedding augmentation steps on
five common VL tasks: image-sentence retrieval, image captioning, visual
question answering, phrase grounding, and text-to-clip retrieval. Our
experiments provide some striking results; an average embedding language model
outperforms an LSTM on retrieval-style tasks; state-of-the-art representations
such as BERT perform relatively poorly on vision-language tasks. From this
comprehensive set of experiments we propose a set of best practices for
incorporating the language component of VL tasks. To further elevate language
features, we also show that knowledge in vision-language problems can be
transferred across tasks to gain performance with multi-task training. This
multi-task training is applied to a new Graph Oriented Vision-Language
Embedding (GrOVLE), which we adapt from Word2Vec using WordNet and an original
visual-language graph built from Visual Genome, providing a ready-to-use
vision-language embedding: http://ai.bu.edu/grovle. | 1 | zrz@andrew.cmu.edu [SEP] Language Features Matter: Effective Language Representations for Vision-Language Tasks : Shouldn't language and vision features be treated equally in vision-language
(VL) tasks? Many VL approaches treat the language component as an afterthought,
using simple language models that are either built upon fixed word embeddings
trained on text-only data or are learned from scratch. We believe that language
features deserve more attention, and conduct experiments which compare
different word embeddings, language models, and embedding augmentation steps on
five common VL tasks: image-sentence retrieval, image captioning, visual
question answering, phrase grounding, and text-to-clip retrieval. Our
experiments provide some striking results; an average embedding language model
outperforms an LSTM on retrieval-style tasks; state-of-the-art representations
such as BERT perform relatively poorly on vision-language tasks. From this
comprehensive set of experiments we propose a set of best practices for
incorporating the language component of VL tasks. To further elevate language
features, we also show that knowledge in vision-language problems can be
transferred across tasks to gain performance with multi-task training. This
multi-task training is applied to a new Graph Oriented Vision-Language
Embedding (GrOVLE), which we adapt from Word2Vec using WordNet and an original
visual-language graph built from Visual Genome, providing a ready-to-use
vision-language embedding: http://ai.bu.edu/grovle. | 359 |
D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic Arm | Cable transmission enables motors of robotic arm to operate lightweight and
low-inertia joints remotely in various environments, but it also creates issues
with motion coupling and cable routing that can reduce arm's control precision
and performance. In this paper, we present a novel motion decoupling mechanism
with low-friction to align the cables and efficiently transmit the motor's
power. By arranging these mechanisms at the joints, we fabricate a fully
decoupled and lightweight cable-driven robotic arm called D3-Arm with all the
electrical components be placed at the base. Its 776 mm length moving part
boasts six degrees of freedom (DOF) and only 1.6 kg weights. To address the
issue of cable slack, a cable-pretension mechanism is integrated to enhance the
stability of long-distance cable transmission. Through a series of
comprehensive tests, D3-Arm demonstrated 1.29 mm average positioning error and
2.0 kg payload capacity, proving the practicality of the proposed decoupling
mechanisms in cable-driven robotic arm. | Liked | jechoi@andrew.cmu.edu | D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic Arm : Cable transmission enables motors of robotic arm to operate lightweight and
low-inertia joints remotely in various environments, but it also creates issues
with motion coupling and cable routing that can reduce arm's control precision
and performance. In this paper, we present a novel motion decoupling mechanism
with low-friction to align the cables and efficiently transmit the motor's
power. By arranging these mechanisms at the joints, we fabricate a fully
decoupled and lightweight cable-driven robotic arm called D3-Arm with all the
electrical components be placed at the base. Its 776 mm length moving part
boasts six degrees of freedom (DOF) and only 1.6 kg weights. To address the
issue of cable slack, a cable-pretension mechanism is integrated to enhance the
stability of long-distance cable transmission. Through a series of
comprehensive tests, D3-Arm demonstrated 1.29 mm average positioning error and
2.0 kg payload capacity, proving the practicality of the proposed decoupling
mechanisms in cable-driven robotic arm. | 1 | jechoi@andrew.cmu.edu [SEP] D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic Arm : Cable transmission enables motors of robotic arm to operate lightweight and
low-inertia joints remotely in various environments, but it also creates issues
with motion coupling and cable routing that can reduce arm's control precision
and performance. In this paper, we present a novel motion decoupling mechanism
with low-friction to align the cables and efficiently transmit the motor's
power. By arranging these mechanisms at the joints, we fabricate a fully
decoupled and lightweight cable-driven robotic arm called D3-Arm with all the
electrical components be placed at the base. Its 776 mm length moving part
boasts six degrees of freedom (DOF) and only 1.6 kg weights. To address the
issue of cable slack, a cable-pretension mechanism is integrated to enhance the
stability of long-distance cable transmission. Through a series of
comprehensive tests, D3-Arm demonstrated 1.29 mm average positioning error and
2.0 kg payload capacity, proving the practicality of the proposed decoupling
mechanisms in cable-driven robotic arm. | 432 |
Machine Learning with a Reject Option: A survey | Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with rejection recently gained interest. This
machine learning subfield enables machine learning models to abstain from
making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with rejection.
We introduce the conditions leading to two types of rejection, ambiguity and
novelty rejection, which we carefully formalize. Moreover, we review and
categorize strategies to evaluate a model's predictive and rejective quality.
Additionally, we define the existing architectures for models with rejection
and describe the standard techniques for learning such models. Finally, we
provide examples of relevant application domains and show how machine learning
with rejection relates to other machine learning research areas. | Disliked | zrz@andrew.cmu.edu | Machine Learning with a Reject Option: A survey : Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with rejection recently gained interest. This
machine learning subfield enables machine learning models to abstain from
making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with rejection.
We introduce the conditions leading to two types of rejection, ambiguity and
novelty rejection, which we carefully formalize. Moreover, we review and
categorize strategies to evaluate a model's predictive and rejective quality.
Additionally, we define the existing architectures for models with rejection
and describe the standard techniques for learning such models. Finally, we
provide examples of relevant application domains and show how machine learning
with rejection relates to other machine learning research areas. | 0 | zrz@andrew.cmu.edu [SEP] Machine Learning with a Reject Option: A survey : Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with rejection recently gained interest. This
machine learning subfield enables machine learning models to abstain from
making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with rejection.
We introduce the conditions leading to two types of rejection, ambiguity and
novelty rejection, which we carefully formalize. Moreover, we review and
categorize strategies to evaluate a model's predictive and rejective quality.
Additionally, we define the existing architectures for models with rejection
and describe the standard techniques for learning such models. Finally, we
provide examples of relevant application domains and show how machine learning
with rejection relates to other machine learning research areas. | 146 |
Understanding Shared Control for Assistive Robotic Arms | Living a self-determined life independent of human caregivers or fully
autonomous robots is a crucial factor for human dignity and the preservation of
self-worth for people with motor impairments. Assistive robotic solutions -
particularly robotic arms - are frequently deployed in domestic care,
empowering people with motor impairments in performing ADLs independently.
However, while assistive robotic arms can help them perform ADLs, currently
available controls are highly complex and time-consuming due to the need to
control multiple DoFs at once and necessary mode-switches. This work provides
an overview of shared control approaches for assistive robotic arms, which aim
to improve their ease of use for people with motor impairments. We identify
three main takeaways for future research: Less is More, Pick-and-Place Matters,
and Communicating Intent. | Liked | jechoi@andrew.cmu.edu | Understanding Shared Control for Assistive Robotic Arms : Living a self-determined life independent of human caregivers or fully
autonomous robots is a crucial factor for human dignity and the preservation of
self-worth for people with motor impairments. Assistive robotic solutions -
particularly robotic arms - are frequently deployed in domestic care,
empowering people with motor impairments in performing ADLs independently.
However, while assistive robotic arms can help them perform ADLs, currently
available controls are highly complex and time-consuming due to the need to
control multiple DoFs at once and necessary mode-switches. This work provides
an overview of shared control approaches for assistive robotic arms, which aim
to improve their ease of use for people with motor impairments. We identify
three main takeaways for future research: Less is More, Pick-and-Place Matters,
and Communicating Intent. | 1 | jechoi@andrew.cmu.edu [SEP] Understanding Shared Control for Assistive Robotic Arms : Living a self-determined life independent of human caregivers or fully
autonomous robots is a crucial factor for human dignity and the preservation of
self-worth for people with motor impairments. Assistive robotic solutions -
particularly robotic arms - are frequently deployed in domestic care,
empowering people with motor impairments in performing ADLs independently.
However, while assistive robotic arms can help them perform ADLs, currently
available controls are highly complex and time-consuming due to the need to
control multiple DoFs at once and necessary mode-switches. This work provides
an overview of shared control approaches for assistive robotic arms, which aim
to improve their ease of use for people with motor impairments. We identify
three main takeaways for future research: Less is More, Pick-and-Place Matters,
and Communicating Intent. | 452 |
Energy-Harvesting Distributed Machine Learning | This paper provides a first study of utilizing energy harvesting for
sustainable machine learning in distributed networks. We consider a distributed
learning setup in which a machine learning model is trained over a large number
of devices that can harvest energy from the ambient environment, and develop a
practical learning framework with theoretical convergence guarantees. We
demonstrate through numerical experiments that the proposed framework can
significantly outperform energy-agnostic benchmarks. Our framework is scalable,
requires only local estimation of the energy statistics, and can be applied to
a wide range of distributed training settings, including machine learning in
wireless networks, edge computing, and mobile internet of things. | Liked | zrz@andrew.cmu.edu | Energy-Harvesting Distributed Machine Learning : This paper provides a first study of utilizing energy harvesting for
sustainable machine learning in distributed networks. We consider a distributed
learning setup in which a machine learning model is trained over a large number
of devices that can harvest energy from the ambient environment, and develop a
practical learning framework with theoretical convergence guarantees. We
demonstrate through numerical experiments that the proposed framework can
significantly outperform energy-agnostic benchmarks. Our framework is scalable,
requires only local estimation of the energy statistics, and can be applied to
a wide range of distributed training settings, including machine learning in
wireless networks, edge computing, and mobile internet of things. | 1 | zrz@andrew.cmu.edu [SEP] Energy-Harvesting Distributed Machine Learning : This paper provides a first study of utilizing energy harvesting for
sustainable machine learning in distributed networks. We consider a distributed
learning setup in which a machine learning model is trained over a large number
of devices that can harvest energy from the ambient environment, and develop a
practical learning framework with theoretical convergence guarantees. We
demonstrate through numerical experiments that the proposed framework can
significantly outperform energy-agnostic benchmarks. Our framework is scalable,
requires only local estimation of the energy statistics, and can be applied to
a wide range of distributed training settings, including machine learning in
wireless networks, edge computing, and mobile internet of things. | 85 |
Acceleration method for generating perception failure scenarios based on editing Markov process | With the rapid advancement of autonomous driving technology, self-driving
cars have become a central focus in the development of future transportation
systems. Scenario generation technology has emerged as a crucial tool for
testing and verifying the safety performance of autonomous driving systems.
Current research in scenario generation primarily focuses on open roads such as
highways, with relatively limited studies on underground parking garages. The
unique structural constraints, insufficient lighting, and high-density
obstacles in underground parking garages impose greater demands on the
perception systems, which are critical to autonomous driving technology.
This study proposes an accelerated generation method for perception failure
scenarios tailored to the underground parking garage environment, aimed at
testing and improving the safety performance of autonomous vehicle (AV)
perception algorithms in such settings. The method presented in this paper
generates an intelligent testing environment with a high density of perception
failure scenarios by learning the interactions between background vehicles
(BVs) and autonomous vehicles (AVs) within perception failure scenarios.
Furthermore, this method edits the Markov process within the perception failure
scenario data to increase the density of critical information in the training
data, thereby optimizing the learning and generation of perception failure
scenarios. A simulation environment for an underground parking garage was
developed using the Carla and Vissim platforms, with Bevfusion employed as the
perception algorithm for testing. The study demonstrates that this method can
generate an intelligent testing environment with a high density of perception
failure scenarios and enhance the safety performance of perception algorithms
within this experimental setup. | Liked | zrz@andrew.cmu.edu | Acceleration method for generating perception failure scenarios based on editing Markov process : With the rapid advancement of autonomous driving technology, self-driving
cars have become a central focus in the development of future transportation
systems. Scenario generation technology has emerged as a crucial tool for
testing and verifying the safety performance of autonomous driving systems.
Current research in scenario generation primarily focuses on open roads such as
highways, with relatively limited studies on underground parking garages. The
unique structural constraints, insufficient lighting, and high-density
obstacles in underground parking garages impose greater demands on the
perception systems, which are critical to autonomous driving technology.
This study proposes an accelerated generation method for perception failure
scenarios tailored to the underground parking garage environment, aimed at
testing and improving the safety performance of autonomous vehicle (AV)
perception algorithms in such settings. The method presented in this paper
generates an intelligent testing environment with a high density of perception
failure scenarios by learning the interactions between background vehicles
(BVs) and autonomous vehicles (AVs) within perception failure scenarios.
Furthermore, this method edits the Markov process within the perception failure
scenario data to increase the density of critical information in the training
data, thereby optimizing the learning and generation of perception failure
scenarios. A simulation environment for an underground parking garage was
developed using the Carla and Vissim platforms, with Bevfusion employed as the
perception algorithm for testing. The study demonstrates that this method can
generate an intelligent testing environment with a high density of perception
failure scenarios and enhance the safety performance of perception algorithms
within this experimental setup. | 1 | zrz@andrew.cmu.edu [SEP] Acceleration method for generating perception failure scenarios based on editing Markov process : With the rapid advancement of autonomous driving technology, self-driving
cars have become a central focus in the development of future transportation
systems. Scenario generation technology has emerged as a crucial tool for
testing and verifying the safety performance of autonomous driving systems.
Current research in scenario generation primarily focuses on open roads such as
highways, with relatively limited studies on underground parking garages. The
unique structural constraints, insufficient lighting, and high-density
obstacles in underground parking garages impose greater demands on the
perception systems, which are critical to autonomous driving technology.
This study proposes an accelerated generation method for perception failure
scenarios tailored to the underground parking garage environment, aimed at
testing and improving the safety performance of autonomous vehicle (AV)
perception algorithms in such settings. The method presented in this paper
generates an intelligent testing environment with a high density of perception
failure scenarios by learning the interactions between background vehicles
(BVs) and autonomous vehicles (AVs) within perception failure scenarios.
Furthermore, this method edits the Markov process within the perception failure
scenario data to increase the density of critical information in the training
data, thereby optimizing the learning and generation of perception failure
scenarios. A simulation environment for an underground parking garage was
developed using the Carla and Vissim platforms, with Bevfusion employed as the
perception algorithm for testing. The study demonstrates that this method can
generate an intelligent testing environment with a high density of perception
failure scenarios and enhance the safety performance of perception algorithms
within this experimental setup. | 273 |
Development of a Voice Controlled Robotic Arm | This paper describes a robotic arm with 5 degrees-of-freedom (DOF) which is
controlled by human voice and has been developed in the Mechatronics
Laboratory, CUET. This robotic arm is interfaced with a PC by serial
communication (RS-232). Users' voice command is captured by a microphone, and
this voice is processed by software which is made by Microsoft visual studio.
Then the specific signal (obtained by signal processing) is sent to control
unit. The main control unit that is used in the robotic arm is a
microcontroller whose model no. is PIC18f452. Then Control unit drives the
actuators, (Hitec HS-422, HS-81) according to the signal or signals to give
required motion of the robotic arm. At present the robotic arm can perform a
set action like pick & pull, gripping, holding & releasing, and some other
extra function like dance-like movement, and can turn according to the voice
commands. | Disliked | jechoi@andrew.cmu.edu | Development of a Voice Controlled Robotic Arm : This paper describes a robotic arm with 5 degrees-of-freedom (DOF) which is
controlled by human voice and has been developed in the Mechatronics
Laboratory, CUET. This robotic arm is interfaced with a PC by serial
communication (RS-232). Users' voice command is captured by a microphone, and
this voice is processed by software which is made by Microsoft visual studio.
Then the specific signal (obtained by signal processing) is sent to control
unit. The main control unit that is used in the robotic arm is a
microcontroller whose model no. is PIC18f452. Then Control unit drives the
actuators, (Hitec HS-422, HS-81) according to the signal or signals to give
required motion of the robotic arm. At present the robotic arm can perform a
set action like pick & pull, gripping, holding & releasing, and some other
extra function like dance-like movement, and can turn according to the voice
commands. | 0 | jechoi@andrew.cmu.edu [SEP] Development of a Voice Controlled Robotic Arm : This paper describes a robotic arm with 5 degrees-of-freedom (DOF) which is
controlled by human voice and has been developed in the Mechatronics
Laboratory, CUET. This robotic arm is interfaced with a PC by serial
communication (RS-232). Users' voice command is captured by a microphone, and
this voice is processed by software which is made by Microsoft visual studio.
Then the specific signal (obtained by signal processing) is sent to control
unit. The main control unit that is used in the robotic arm is a
microcontroller whose model no. is PIC18f452. Then Control unit drives the
actuators, (Hitec HS-422, HS-81) according to the signal or signals to give
required motion of the robotic arm. At present the robotic arm can perform a
set action like pick & pull, gripping, holding & releasing, and some other
extra function like dance-like movement, and can turn according to the voice
commands. | 426 |
Casting manipulation of unknown string by robot arm | Casting manipulation has been studied to expand the robot's movable range. In
this manipulation, the robot throws and reaches the end effector to a distant
target. Usually, a special casting manipulator, which consists of rigid arm
links and specific flexible linear objects, is constructed for an effective
casting manipulation. However, the special manipulator cannot perform normal
manipulations, such as picking and placing, grasping, and operating objects. We
propose that the normal robot arm, which can perform normal tasks, picks up an
unknown string in the surrounding environment and realizes casting manipulation
with it. As the properties of the string are not provided in advance, it is
crucial how to reflect it in casting manipulation. This is realized by the
motion generation of the robot arm with the simulation of string movement,
actual string manipulation by the robot arm, and string parameter estimation
from the actual string movement. After repeating these three steps, the
simulated string movement approximates the actual to realize casting
manipulation with the unknown string. We confirmed the effectiveness of the
proposed method through experiments. The try of this study will lead to
enhancement of the performance of home service robot, exploration robot, rescue
robot and entertainment robot. | Liked | jechoi@andrew.cmu.edu | Casting manipulation of unknown string by robot arm : Casting manipulation has been studied to expand the robot's movable range. In
this manipulation, the robot throws and reaches the end effector to a distant
target. Usually, a special casting manipulator, which consists of rigid arm
links and specific flexible linear objects, is constructed for an effective
casting manipulation. However, the special manipulator cannot perform normal
manipulations, such as picking and placing, grasping, and operating objects. We
propose that the normal robot arm, which can perform normal tasks, picks up an
unknown string in the surrounding environment and realizes casting manipulation
with it. As the properties of the string are not provided in advance, it is
crucial how to reflect it in casting manipulation. This is realized by the
motion generation of the robot arm with the simulation of string movement,
actual string manipulation by the robot arm, and string parameter estimation
from the actual string movement. After repeating these three steps, the
simulated string movement approximates the actual to realize casting
manipulation with the unknown string. We confirmed the effectiveness of the
proposed method through experiments. The try of this study will lead to
enhancement of the performance of home service robot, exploration robot, rescue
robot and entertainment robot. | 1 | jechoi@andrew.cmu.edu [SEP] Casting manipulation of unknown string by robot arm : Casting manipulation has been studied to expand the robot's movable range. In
this manipulation, the robot throws and reaches the end effector to a distant
target. Usually, a special casting manipulator, which consists of rigid arm
links and specific flexible linear objects, is constructed for an effective
casting manipulation. However, the special manipulator cannot perform normal
manipulations, such as picking and placing, grasping, and operating objects. We
propose that the normal robot arm, which can perform normal tasks, picks up an
unknown string in the surrounding environment and realizes casting manipulation
with it. As the properties of the string are not provided in advance, it is
crucial how to reflect it in casting manipulation. This is realized by the
motion generation of the robot arm with the simulation of string movement,
actual string manipulation by the robot arm, and string parameter estimation
from the actual string movement. After repeating these three steps, the
simulated string movement approximates the actual to realize casting
manipulation with the unknown string. We confirmed the effectiveness of the
proposed method through experiments. The try of this study will lead to
enhancement of the performance of home service robot, exploration robot, rescue
robot and entertainment robot. | 458 |
Quantum Neural Networks: Concepts, Applications, and Challenges | Quantum deep learning is a research field for the use of quantum computing
techniques for training deep neural networks. The research topics and
directions of deep learning and quantum computing have been separated for long
time, however by discovering that quantum circuits can act like artificial
neural networks, quantum deep learning research is widely adopted. This paper
explains the backgrounds and basic principles of quantum deep learning and also
introduces major achievements. After that, this paper discusses the challenges
of quantum deep learning research in multiple perspectives. Lastly, this paper
presents various future research directions and application fields of quantum
deep learning. | Disliked | zrz@andrew.cmu.edu | Quantum Neural Networks: Concepts, Applications, and Challenges : Quantum deep learning is a research field for the use of quantum computing
techniques for training deep neural networks. The research topics and
directions of deep learning and quantum computing have been separated for long
time, however by discovering that quantum circuits can act like artificial
neural networks, quantum deep learning research is widely adopted. This paper
explains the backgrounds and basic principles of quantum deep learning and also
introduces major achievements. After that, this paper discusses the challenges
of quantum deep learning research in multiple perspectives. Lastly, this paper
presents various future research directions and application fields of quantum
deep learning. | 0 | zrz@andrew.cmu.edu [SEP] Quantum Neural Networks: Concepts, Applications, and Challenges : Quantum deep learning is a research field for the use of quantum computing
techniques for training deep neural networks. The research topics and
directions of deep learning and quantum computing have been separated for long
time, however by discovering that quantum circuits can act like artificial
neural networks, quantum deep learning research is widely adopted. This paper
explains the backgrounds and basic principles of quantum deep learning and also
introduces major achievements. After that, this paper discusses the challenges
of quantum deep learning research in multiple perspectives. Lastly, this paper
presents various future research directions and application fields of quantum
deep learning. | 166 |
Integrating Learning and Reasoning with Deep Logic Models | Deep learning is very effective at jointly learning feature representations
and classification models, especially when dealing with high dimensional input
patterns. Probabilistic logic reasoning, on the other hand, is capable to take
consistent and robust decisions in complex environments. The integration of
deep learning and logic reasoning is still an open-research problem and it is
considered to be the key for the development of real intelligent agents. This
paper presents Deep Logic Models, which are deep graphical models integrating
deep learning and logic reasoning both for learning and inference. Deep Logic
Models create an end-to-end differentiable architecture, where deep learners
are embedded into a network implementing a continuous relaxation of the logic
knowledge. The learning process allows to jointly learn the weights of the deep
learners and the meta-parameters controlling the high-level reasoning. The
experimental results show that the proposed methodology overtakes the
limitations of the other approaches that have been proposed to bridge deep
learning and reasoning. | Liked | zrz@andrew.cmu.edu | Integrating Learning and Reasoning with Deep Logic Models : Deep learning is very effective at jointly learning feature representations
and classification models, especially when dealing with high dimensional input
patterns. Probabilistic logic reasoning, on the other hand, is capable to take
consistent and robust decisions in complex environments. The integration of
deep learning and logic reasoning is still an open-research problem and it is
considered to be the key for the development of real intelligent agents. This
paper presents Deep Logic Models, which are deep graphical models integrating
deep learning and logic reasoning both for learning and inference. Deep Logic
Models create an end-to-end differentiable architecture, where deep learners
are embedded into a network implementing a continuous relaxation of the logic
knowledge. The learning process allows to jointly learn the weights of the deep
learners and the meta-parameters controlling the high-level reasoning. The
experimental results show that the proposed methodology overtakes the
limitations of the other approaches that have been proposed to bridge deep
learning and reasoning. | 1 | zrz@andrew.cmu.edu [SEP] Integrating Learning and Reasoning with Deep Logic Models : Deep learning is very effective at jointly learning feature representations
and classification models, especially when dealing with high dimensional input
patterns. Probabilistic logic reasoning, on the other hand, is capable to take
consistent and robust decisions in complex environments. The integration of
deep learning and logic reasoning is still an open-research problem and it is
considered to be the key for the development of real intelligent agents. This
paper presents Deep Logic Models, which are deep graphical models integrating
deep learning and logic reasoning both for learning and inference. Deep Logic
Models create an end-to-end differentiable architecture, where deep learners
are embedded into a network implementing a continuous relaxation of the logic
knowledge. The learning process allows to jointly learn the weights of the deep
learners and the meta-parameters controlling the high-level reasoning. The
experimental results show that the proposed methodology overtakes the
limitations of the other approaches that have been proposed to bridge deep
learning and reasoning. | 170 |
Diversity in Machine Learning | Machine learning methods have achieved good performance and been widely
applied in various real-world applications. They can learn the model adaptively
and be better fit for special requirements of different tasks. Generally, a
good machine learning system is composed of plentiful training data, a good
model training process, and an accurate inference. Many factors can affect the
performance of the machine learning process, among which the diversity of the
machine learning process is an important one. The diversity can help each
procedure to guarantee a total good machine learning: diversity of the training
data ensures that the training data can provide more discriminative information
for the model, diversity of the learned model (diversity in parameters of each
model or diversity among different base models) makes each parameter/model
capture unique or complement information and the diversity in inference can
provide multiple choices each of which corresponds to a specific plausible
local optimal result. Even though the diversity plays an important role in
machine learning process, there is no systematical analysis of the
diversification in machine learning system. In this paper, we systematically
summarize the methods to make data diversification, model diversification, and
inference diversification in the machine learning process, respectively. In
addition, the typical applications where the diversity technology improved the
machine learning performance have been surveyed, including the remote sensing
imaging tasks, machine translation, camera relocalization, image segmentation,
object detection, topic modeling, and others. Finally, we discuss some
challenges of the diversity technology in machine learning and point out some
directions in future work. | Liked | zrz@andrew.cmu.edu | Diversity in Machine Learning : Machine learning methods have achieved good performance and been widely
applied in various real-world applications. They can learn the model adaptively
and be better fit for special requirements of different tasks. Generally, a
good machine learning system is composed of plentiful training data, a good
model training process, and an accurate inference. Many factors can affect the
performance of the machine learning process, among which the diversity of the
machine learning process is an important one. The diversity can help each
procedure to guarantee a total good machine learning: diversity of the training
data ensures that the training data can provide more discriminative information
for the model, diversity of the learned model (diversity in parameters of each
model or diversity among different base models) makes each parameter/model
capture unique or complement information and the diversity in inference can
provide multiple choices each of which corresponds to a specific plausible
local optimal result. Even though the diversity plays an important role in
machine learning process, there is no systematical analysis of the
diversification in machine learning system. In this paper, we systematically
summarize the methods to make data diversification, model diversification, and
inference diversification in the machine learning process, respectively. In
addition, the typical applications where the diversity technology improved the
machine learning performance have been surveyed, including the remote sensing
imaging tasks, machine translation, camera relocalization, image segmentation,
object detection, topic modeling, and others. Finally, we discuss some
challenges of the diversity technology in machine learning and point out some
directions in future work. | 1 | zrz@andrew.cmu.edu [SEP] Diversity in Machine Learning : Machine learning methods have achieved good performance and been widely
applied in various real-world applications. They can learn the model adaptively
and be better fit for special requirements of different tasks. Generally, a
good machine learning system is composed of plentiful training data, a good
model training process, and an accurate inference. Many factors can affect the
performance of the machine learning process, among which the diversity of the
machine learning process is an important one. The diversity can help each
procedure to guarantee a total good machine learning: diversity of the training
data ensures that the training data can provide more discriminative information
for the model, diversity of the learned model (diversity in parameters of each
model or diversity among different base models) makes each parameter/model
capture unique or complement information and the diversity in inference can
provide multiple choices each of which corresponds to a specific plausible
local optimal result. Even though the diversity plays an important role in
machine learning process, there is no systematical analysis of the
diversification in machine learning system. In this paper, we systematically
summarize the methods to make data diversification, model diversification, and
inference diversification in the machine learning process, respectively. In
addition, the typical applications where the diversity technology improved the
machine learning performance have been surveyed, including the remote sensing
imaging tasks, machine translation, camera relocalization, image segmentation,
object detection, topic modeling, and others. Finally, we discuss some
challenges of the diversity technology in machine learning and point out some
directions in future work. | 134 |
A Neuro-Symbolic Humanlike Arm Controller for Sophia the Robot | We outline the design and construction of novel robotic arms using machine
perception, convolutional neural networks, and symbolic AI for logical control
and affordance indexing. We describe our robotic arms built with a humanlike
mechanical configuration and aesthetic, with 28 degrees of freedom, touch
sensors, and series elastic actuators. The arms were modelled in Roodle and
Gazebo with URDF models, as well as Unity, and implement motion control
solutions for solving live games of Baccarat (the casino card game), rock paper
scissors, handshaking, and drawing. This includes live interactions with
people, incorporating both social control of the hands and facial gestures, and
physical inverse kinematics (IK) for grasping and manipulation tasks. The
resulting framework is an integral part of the Sophia 2020 alpha platform,
which is being used with ongoing research in the authors work with team AHAM,
an ANA Avatar Xprize effort towards human-AI hybrid telepresence. These results
are available to test on the broadly released Hanson Robotics Sophia 2020 robot
platform, for users to try and extend. | Disliked | jechoi@andrew.cmu.edu | A Neuro-Symbolic Humanlike Arm Controller for Sophia the Robot : We outline the design and construction of novel robotic arms using machine
perception, convolutional neural networks, and symbolic AI for logical control
and affordance indexing. We describe our robotic arms built with a humanlike
mechanical configuration and aesthetic, with 28 degrees of freedom, touch
sensors, and series elastic actuators. The arms were modelled in Roodle and
Gazebo with URDF models, as well as Unity, and implement motion control
solutions for solving live games of Baccarat (the casino card game), rock paper
scissors, handshaking, and drawing. This includes live interactions with
people, incorporating both social control of the hands and facial gestures, and
physical inverse kinematics (IK) for grasping and manipulation tasks. The
resulting framework is an integral part of the Sophia 2020 alpha platform,
which is being used with ongoing research in the authors work with team AHAM,
an ANA Avatar Xprize effort towards human-AI hybrid telepresence. These results
are available to test on the broadly released Hanson Robotics Sophia 2020 robot
platform, for users to try and extend. | 0 | jechoi@andrew.cmu.edu [SEP] A Neuro-Symbolic Humanlike Arm Controller for Sophia the Robot : We outline the design and construction of novel robotic arms using machine
perception, convolutional neural networks, and symbolic AI for logical control
and affordance indexing. We describe our robotic arms built with a humanlike
mechanical configuration and aesthetic, with 28 degrees of freedom, touch
sensors, and series elastic actuators. The arms were modelled in Roodle and
Gazebo with URDF models, as well as Unity, and implement motion control
solutions for solving live games of Baccarat (the casino card game), rock paper
scissors, handshaking, and drawing. This includes live interactions with
people, incorporating both social control of the hands and facial gestures, and
physical inverse kinematics (IK) for grasping and manipulation tasks. The
resulting framework is an integral part of the Sophia 2020 alpha platform,
which is being used with ongoing research in the authors work with team AHAM,
an ANA Avatar Xprize effort towards human-AI hybrid telepresence. These results
are available to test on the broadly released Hanson Robotics Sophia 2020 robot
platform, for users to try and extend. | 439 |
Prismatic Soft Actuator Augments the Workspace of Soft Continuum Robots | Soft robots are promising for manipulation tasks thanks to their compliance,
safety, and high degree of freedom. However, the commonly used bidirectional
continuum segment design means soft robotic manipulators only function in a
limited hemispherical workspace. This work increases a soft robotic arm's
workspace by designing, fabricating, and controlling an additional soft
prismatic actuator at the base of the soft arm. This actuator consists of
pneumatic artificial muscles and a piston, making the actuator back-driveable.
We increase the task space volume by 116\%, and we are now able to perform
manipulation tasks that were previously impossible for soft robots, such as
picking and placing objects at different positions on a surface and grabbing an
object out of a container. By combining a soft robotic arm with a prismatic
joint, we greatly increase the usability of soft robots for object
manipulation. This work promotes the use of integrated and modular soft robotic
systems for practical manipulation applications in human-centered environments. | Liked | jechoi@andrew.cmu.edu | Prismatic Soft Actuator Augments the Workspace of Soft Continuum Robots : Soft robots are promising for manipulation tasks thanks to their compliance,
safety, and high degree of freedom. However, the commonly used bidirectional
continuum segment design means soft robotic manipulators only function in a
limited hemispherical workspace. This work increases a soft robotic arm's
workspace by designing, fabricating, and controlling an additional soft
prismatic actuator at the base of the soft arm. This actuator consists of
pneumatic artificial muscles and a piston, making the actuator back-driveable.
We increase the task space volume by 116\%, and we are now able to perform
manipulation tasks that were previously impossible for soft robots, such as
picking and placing objects at different positions on a surface and grabbing an
object out of a container. By combining a soft robotic arm with a prismatic
joint, we greatly increase the usability of soft robots for object
manipulation. This work promotes the use of integrated and modular soft robotic
systems for practical manipulation applications in human-centered environments. | 1 | jechoi@andrew.cmu.edu [SEP] Prismatic Soft Actuator Augments the Workspace of Soft Continuum Robots : Soft robots are promising for manipulation tasks thanks to their compliance,
safety, and high degree of freedom. However, the commonly used bidirectional
continuum segment design means soft robotic manipulators only function in a
limited hemispherical workspace. This work increases a soft robotic arm's
workspace by designing, fabricating, and controlling an additional soft
prismatic actuator at the base of the soft arm. This actuator consists of
pneumatic artificial muscles and a piston, making the actuator back-driveable.
We increase the task space volume by 116\%, and we are now able to perform
manipulation tasks that were previously impossible for soft robots, such as
picking and placing objects at different positions on a surface and grabbing an
object out of a container. By combining a soft robotic arm with a prismatic
joint, we greatly increase the usability of soft robots for object
manipulation. This work promotes the use of integrated and modular soft robotic
systems for practical manipulation applications in human-centered environments. | 480 |
On-the-Fly Learning in a Perpetual Learning Machine | Despite the promise of brain-inspired machine learning, deep neural networks
(DNN) have frustratingly failed to bridge the deceptively large gap between
learning and memory. Here, we introduce a Perpetual Learning Machine; a new
type of DNN that is capable of brain-like dynamic 'on the fly' learning because
it exists in a self-supervised state of Perpetual Stochastic Gradient Descent.
Thus, we provide the means to unify learning and memory within a machine
learning framework. We also explore the elegant duality of abstraction and
synthesis: the Yin and Yang of deep learning. | Liked | zrz@andrew.cmu.edu | On-the-Fly Learning in a Perpetual Learning Machine : Despite the promise of brain-inspired machine learning, deep neural networks
(DNN) have frustratingly failed to bridge the deceptively large gap between
learning and memory. Here, we introduce a Perpetual Learning Machine; a new
type of DNN that is capable of brain-like dynamic 'on the fly' learning because
it exists in a self-supervised state of Perpetual Stochastic Gradient Descent.
Thus, we provide the means to unify learning and memory within a machine
learning framework. We also explore the elegant duality of abstraction and
synthesis: the Yin and Yang of deep learning. | 1 | zrz@andrew.cmu.edu [SEP] On-the-Fly Learning in a Perpetual Learning Machine : Despite the promise of brain-inspired machine learning, deep neural networks
(DNN) have frustratingly failed to bridge the deceptively large gap between
learning and memory. Here, we introduce a Perpetual Learning Machine; a new
type of DNN that is capable of brain-like dynamic 'on the fly' learning because
it exists in a self-supervised state of Perpetual Stochastic Gradient Descent.
Thus, we provide the means to unify learning and memory within a machine
learning framework. We also explore the elegant duality of abstraction and
synthesis: the Yin and Yang of deep learning. | 67 |
A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives | Recent success of machine learning in many domains has been overwhelming,
which often leads to false expectations regarding the capabilities of behavior
learning in robotics. In this survey, we analyze the current state of machine
learning for robotic behaviors. We will give a broad overview of behaviors that
have been learned and used on real robots. Our focus is on kinematically or
sensorially complex robots. That includes humanoid robots or parts of humanoid
robots, for example, legged robots or robotic arms. We will classify presented
behaviors according to various categories and we will draw conclusions about
what can be learned and what should be learned. Furthermore, we will give an
outlook on problems that are challenging today but might be solved by machine
learning in the future and argue that classical robotics and other approaches
from artificial intelligence should be integrated more with machine learning to
form complete, autonomous systems. | Liked | jechoi@andrew.cmu.edu | A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives : Recent success of machine learning in many domains has been overwhelming,
which often leads to false expectations regarding the capabilities of behavior
learning in robotics. In this survey, we analyze the current state of machine
learning for robotic behaviors. We will give a broad overview of behaviors that
have been learned and used on real robots. Our focus is on kinematically or
sensorially complex robots. That includes humanoid robots or parts of humanoid
robots, for example, legged robots or robotic arms. We will classify presented
behaviors according to various categories and we will draw conclusions about
what can be learned and what should be learned. Furthermore, we will give an
outlook on problems that are challenging today but might be solved by machine
learning in the future and argue that classical robotics and other approaches
from artificial intelligence should be integrated more with machine learning to
form complete, autonomous systems. | 1 | jechoi@andrew.cmu.edu [SEP] A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives : Recent success of machine learning in many domains has been overwhelming,
which often leads to false expectations regarding the capabilities of behavior
learning in robotics. In this survey, we analyze the current state of machine
learning for robotic behaviors. We will give a broad overview of behaviors that
have been learned and used on real robots. Our focus is on kinematically or
sensorially complex robots. That includes humanoid robots or parts of humanoid
robots, for example, legged robots or robotic arms. We will classify presented
behaviors according to various categories and we will draw conclusions about
what can be learned and what should be learned. Furthermore, we will give an
outlook on problems that are challenging today but might be solved by machine
learning in the future and argue that classical robotics and other approaches
from artificial intelligence should be integrated more with machine learning to
form complete, autonomous systems. | 573 |
Probabilistic Generative Deep Learning for Molecular Design | Probabilistic generative deep learning for molecular design involves the
discovery and design of new molecules and analysis of their structure,
properties and activities by probabilistic generative models using the deep
learning approach. It leverages the existing huge databases and publications of
experimental results, and quantum-mechanical calculations, to learn and explore
molecular structure, properties and activities. We discuss the major components
of probabilistic generative deep learning for molecular design, which include
molecular structure, molecular representations, deep generative models,
molecular latent representations and latent space, molecular structure-property
and structure-activity relationships, molecular similarity and molecular
design. We highlight significant recent work using or applicable to this new
approach. | Disliked | zrz@andrew.cmu.edu | Probabilistic Generative Deep Learning for Molecular Design : Probabilistic generative deep learning for molecular design involves the
discovery and design of new molecules and analysis of their structure,
properties and activities by probabilistic generative models using the deep
learning approach. It leverages the existing huge databases and publications of
experimental results, and quantum-mechanical calculations, to learn and explore
molecular structure, properties and activities. We discuss the major components
of probabilistic generative deep learning for molecular design, which include
molecular structure, molecular representations, deep generative models,
molecular latent representations and latent space, molecular structure-property
and structure-activity relationships, molecular similarity and molecular
design. We highlight significant recent work using or applicable to this new
approach. | 0 | zrz@andrew.cmu.edu [SEP] Probabilistic Generative Deep Learning for Molecular Design : Probabilistic generative deep learning for molecular design involves the
discovery and design of new molecules and analysis of their structure,
properties and activities by probabilistic generative models using the deep
learning approach. It leverages the existing huge databases and publications of
experimental results, and quantum-mechanical calculations, to learn and explore
molecular structure, properties and activities. We discuss the major components
of probabilistic generative deep learning for molecular design, which include
molecular structure, molecular representations, deep generative models,
molecular latent representations and latent space, molecular structure-property
and structure-activity relationships, molecular similarity and molecular
design. We highlight significant recent work using or applicable to this new
approach. | 239 |
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning | Most successes in robotic manipulation have been restricted to single-arm
robots, which limits the range of solvable tasks to pick-and-place, insertion,
and objects rearrangement. In contrast, dual and multi arm robot platforms
unlock a rich diversity of problems that can be tackled, such as laundry
folding and executing cooking skills. However, developing controllers for
multi-arm robots is complexified by a number of unique challenges, such as the
need for coordinated bimanual behaviors, and collision avoidance amongst
robots. Given these challenges, in this work we study how to solve bi-manual
tasks using reinforcement learning (RL) trained in simulation, such that the
resulting policies can be executed on real robotic platforms. Our RL approach
results in significant simplifications due to using real-time (4Hz) joint-space
control and directly passing unfiltered observations to neural networks
policies. We also extensively discuss modifications to our simulated
environment which lead to effective training of RL policies. In addition to
designing control algorithms, a key challenge is how to design fair evaluation
tasks for bi-manual robots that stress bimanual coordination, while removing
orthogonal complicating factors such as high-level perception. In this work, we
design a Connect Task, where the aim is for two robot arms to pick up and
attach two blocks with magnetic connection points. We validate our approach
with two xArm6 robots and 3D printed blocks with magnetic attachments, and find
that our system has 100% success rate at picking up blocks, and 65% success
rate at the Connect Task. | Liked | jechoi@andrew.cmu.edu | Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning : Most successes in robotic manipulation have been restricted to single-arm
robots, which limits the range of solvable tasks to pick-and-place, insertion,
and objects rearrangement. In contrast, dual and multi arm robot platforms
unlock a rich diversity of problems that can be tackled, such as laundry
folding and executing cooking skills. However, developing controllers for
multi-arm robots is complexified by a number of unique challenges, such as the
need for coordinated bimanual behaviors, and collision avoidance amongst
robots. Given these challenges, in this work we study how to solve bi-manual
tasks using reinforcement learning (RL) trained in simulation, such that the
resulting policies can be executed on real robotic platforms. Our RL approach
results in significant simplifications due to using real-time (4Hz) joint-space
control and directly passing unfiltered observations to neural networks
policies. We also extensively discuss modifications to our simulated
environment which lead to effective training of RL policies. In addition to
designing control algorithms, a key challenge is how to design fair evaluation
tasks for bi-manual robots that stress bimanual coordination, while removing
orthogonal complicating factors such as high-level perception. In this work, we
design a Connect Task, where the aim is for two robot arms to pick up and
attach two blocks with magnetic connection points. We validate our approach
with two xArm6 robots and 3D printed blocks with magnetic attachments, and find
that our system has 100% success rate at picking up blocks, and 65% success
rate at the Connect Task. | 1 | jechoi@andrew.cmu.edu [SEP] Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning : Most successes in robotic manipulation have been restricted to single-arm
robots, which limits the range of solvable tasks to pick-and-place, insertion,
and objects rearrangement. In contrast, dual and multi arm robot platforms
unlock a rich diversity of problems that can be tackled, such as laundry
folding and executing cooking skills. However, developing controllers for
multi-arm robots is complexified by a number of unique challenges, such as the
need for coordinated bimanual behaviors, and collision avoidance amongst
robots. Given these challenges, in this work we study how to solve bi-manual
tasks using reinforcement learning (RL) trained in simulation, such that the
resulting policies can be executed on real robotic platforms. Our RL approach
results in significant simplifications due to using real-time (4Hz) joint-space
control and directly passing unfiltered observations to neural networks
policies. We also extensively discuss modifications to our simulated
environment which lead to effective training of RL policies. In addition to
designing control algorithms, a key challenge is how to design fair evaluation
tasks for bi-manual robots that stress bimanual coordination, while removing
orthogonal complicating factors such as high-level perception. In this work, we
design a Connect Task, where the aim is for two robot arms to pick up and
attach two blocks with magnetic connection points. We validate our approach
with two xArm6 robots and 3D printed blocks with magnetic attachments, and find
that our system has 100% success rate at picking up blocks, and 65% success
rate at the Connect Task. | 544 |
Controlling Assistive Robots with Learned Latent Actions | Assistive robotic arms enable users with physical disabilities to perform
everyday tasks without relying on a caregiver. Unfortunately, the very
dexterity that makes these arms useful also makes them challenging to
teleoperate: the robot has more degrees-of-freedom than the human can directly
coordinate with a handheld joystick. Our insight is that we can make assistive
robots easier for humans to control by leveraging latent actions. Latent
actions provide a low-dimensional embedding of high-dimensional robot behavior:
for example, one latent dimension might guide the assistive arm along a pouring
motion. In this paper, we design a teleoperation algorithm for assistive robots
that learns latent actions from task demonstrations. We formulate the
controllability, consistency, and scaling properties that user-friendly latent
actions should have, and evaluate how different low-dimensional embeddings
capture these properties. Finally, we conduct two user studies on a robotic arm
to compare our latent action approach to both state-of-the-art shared autonomy
baselines and a teleoperation strategy currently used by assistive arms.
Participants completed assistive eating and cooking tasks more efficiently when
leveraging our latent actions, and also subjectively reported that latent
actions made the task easier to perform. The video accompanying this paper can
be found at: https://youtu.be/wjnhrzugBj4. | Liked | jechoi@andrew.cmu.edu | Controlling Assistive Robots with Learned Latent Actions : Assistive robotic arms enable users with physical disabilities to perform
everyday tasks without relying on a caregiver. Unfortunately, the very
dexterity that makes these arms useful also makes them challenging to
teleoperate: the robot has more degrees-of-freedom than the human can directly
coordinate with a handheld joystick. Our insight is that we can make assistive
robots easier for humans to control by leveraging latent actions. Latent
actions provide a low-dimensional embedding of high-dimensional robot behavior:
for example, one latent dimension might guide the assistive arm along a pouring
motion. In this paper, we design a teleoperation algorithm for assistive robots
that learns latent actions from task demonstrations. We formulate the
controllability, consistency, and scaling properties that user-friendly latent
actions should have, and evaluate how different low-dimensional embeddings
capture these properties. Finally, we conduct two user studies on a robotic arm
to compare our latent action approach to both state-of-the-art shared autonomy
baselines and a teleoperation strategy currently used by assistive arms.
Participants completed assistive eating and cooking tasks more efficiently when
leveraging our latent actions, and also subjectively reported that latent
actions made the task easier to perform. The video accompanying this paper can
be found at: https://youtu.be/wjnhrzugBj4. | 1 | jechoi@andrew.cmu.edu [SEP] Controlling Assistive Robots with Learned Latent Actions : Assistive robotic arms enable users with physical disabilities to perform
everyday tasks without relying on a caregiver. Unfortunately, the very
dexterity that makes these arms useful also makes them challenging to
teleoperate: the robot has more degrees-of-freedom than the human can directly
coordinate with a handheld joystick. Our insight is that we can make assistive
robots easier for humans to control by leveraging latent actions. Latent
actions provide a low-dimensional embedding of high-dimensional robot behavior:
for example, one latent dimension might guide the assistive arm along a pouring
motion. In this paper, we design a teleoperation algorithm for assistive robots
that learns latent actions from task demonstrations. We formulate the
controllability, consistency, and scaling properties that user-friendly latent
actions should have, and evaluate how different low-dimensional embeddings
capture these properties. Finally, we conduct two user studies on a robotic arm
to compare our latent action approach to both state-of-the-art shared autonomy
baselines and a teleoperation strategy currently used by assistive arms.
Participants completed assistive eating and cooking tasks more efficiently when
leveraging our latent actions, and also subjectively reported that latent
actions made the task easier to perform. The video accompanying this paper can
be found at: https://youtu.be/wjnhrzugBj4. | 520 |
Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles | Cooperative perception (CP) is a key technology to facilitate consistent and
accurate situational awareness for connected and autonomous vehicles (CAVs). To
tackle the network resource inefficiency issue in traditional broadcast-based
CP, unicast-based CP has been proposed to associate CAV pairs for cooperative
perception via vehicle-to-vehicle transmission. In this paper, we investigate
unicast-based CP among CAV pairs. With the consideration of dynamic perception
workloads and channel conditions due to vehicle mobility and dynamic radio
resource availability, we propose an adaptive cooperative perception scheme for
CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and
human-driven vehicles. We aim to determine when to switch between cooperative
perception and stand-alone perception for each CAV pair, and allocate
communication and computing resources to cooperative CAV pairs for maximizing
the computing efficiency gain under perception task delay requirements. A
model-assisted multi-agent reinforcement learning (MARL) solution is developed,
which integrates MARL for an adaptive CAV cooperation decision and an
optimization model for communication and computing resource allocation.
Simulation results demonstrate the effectiveness of the proposed scheme in
achieving high computing efficiency gain, as compared with benchmark schemes. | Liked | zrz@andrew.cmu.edu | Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles : Cooperative perception (CP) is a key technology to facilitate consistent and
accurate situational awareness for connected and autonomous vehicles (CAVs). To
tackle the network resource inefficiency issue in traditional broadcast-based
CP, unicast-based CP has been proposed to associate CAV pairs for cooperative
perception via vehicle-to-vehicle transmission. In this paper, we investigate
unicast-based CP among CAV pairs. With the consideration of dynamic perception
workloads and channel conditions due to vehicle mobility and dynamic radio
resource availability, we propose an adaptive cooperative perception scheme for
CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and
human-driven vehicles. We aim to determine when to switch between cooperative
perception and stand-alone perception for each CAV pair, and allocate
communication and computing resources to cooperative CAV pairs for maximizing
the computing efficiency gain under perception task delay requirements. A
model-assisted multi-agent reinforcement learning (MARL) solution is developed,
which integrates MARL for an adaptive CAV cooperation decision and an
optimization model for communication and computing resource allocation.
Simulation results demonstrate the effectiveness of the proposed scheme in
achieving high computing efficiency gain, as compared with benchmark schemes. | 1 | zrz@andrew.cmu.edu [SEP] Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles : Cooperative perception (CP) is a key technology to facilitate consistent and
accurate situational awareness for connected and autonomous vehicles (CAVs). To
tackle the network resource inefficiency issue in traditional broadcast-based
CP, unicast-based CP has been proposed to associate CAV pairs for cooperative
perception via vehicle-to-vehicle transmission. In this paper, we investigate
unicast-based CP among CAV pairs. With the consideration of dynamic perception
workloads and channel conditions due to vehicle mobility and dynamic radio
resource availability, we propose an adaptive cooperative perception scheme for
CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and
human-driven vehicles. We aim to determine when to switch between cooperative
perception and stand-alone perception for each CAV pair, and allocate
communication and computing resources to cooperative CAV pairs for maximizing
the computing efficiency gain under perception task delay requirements. A
model-assisted multi-agent reinforcement learning (MARL) solution is developed,
which integrates MARL for an adaptive CAV cooperation decision and an
optimization model for communication and computing resource allocation.
Simulation results demonstrate the effectiveness of the proposed scheme in
achieving high computing efficiency gain, as compared with benchmark schemes. | 314 |
The many faces of deep learning | Deep learning has sparked a network of mutual interactions between different
disciplines and AI. Naturally, each discipline focuses and interprets the
workings of deep learning in different ways. This diversity of perspectives on
deep learning, from neuroscience to statistical physics, is a rich source of
inspiration that fuels novel developments in the theory and applications of
machine learning. In this perspective, we collect and synthesize different
intuitions scattered across several communities as for how deep learning works.
In particular, we will briefly discuss the different perspectives that
disciplines across mathematics, physics, computation, and neuroscience take on
how deep learning does its tricks. Our discussion on each perspective is
necessarily shallow due to the multiple views that had to be covered. The
deepness in this case should come from putting all these faces of deep learning
together in the reader's mind, so that one can look at the same problem from
different angles. | Disliked | zrz@andrew.cmu.edu | The many faces of deep learning : Deep learning has sparked a network of mutual interactions between different
disciplines and AI. Naturally, each discipline focuses and interprets the
workings of deep learning in different ways. This diversity of perspectives on
deep learning, from neuroscience to statistical physics, is a rich source of
inspiration that fuels novel developments in the theory and applications of
machine learning. In this perspective, we collect and synthesize different
intuitions scattered across several communities as for how deep learning works.
In particular, we will briefly discuss the different perspectives that
disciplines across mathematics, physics, computation, and neuroscience take on
how deep learning does its tricks. Our discussion on each perspective is
necessarily shallow due to the multiple views that had to be covered. The
deepness in this case should come from putting all these faces of deep learning
together in the reader's mind, so that one can look at the same problem from
different angles. | 0 | zrz@andrew.cmu.edu [SEP] The many faces of deep learning : Deep learning has sparked a network of mutual interactions between different
disciplines and AI. Naturally, each discipline focuses and interprets the
workings of deep learning in different ways. This diversity of perspectives on
deep learning, from neuroscience to statistical physics, is a rich source of
inspiration that fuels novel developments in the theory and applications of
machine learning. In this perspective, we collect and synthesize different
intuitions scattered across several communities as for how deep learning works.
In particular, we will briefly discuss the different perspectives that
disciplines across mathematics, physics, computation, and neuroscience take on
how deep learning does its tricks. Our discussion on each perspective is
necessarily shallow due to the multiple views that had to be covered. The
deepness in this case should come from putting all these faces of deep learning
together in the reader's mind, so that one can look at the same problem from
different angles. | 227 |
Fast Heuristic Scheduling and Trajectory Planning for Robotic Fruit Harvesters with Multiple Cartesian Arms | This work proposes a fast heuristic algorithm for the coupled scheduling and
trajectory planning of multiple Cartesian robotic arms harvesting fruits. Our
method partitions the workspace, assigns fruit-picking sequences to arms,
determines tight and feasible fruit-picking schedules and vehicle travel speed,
and generates smooth, collision-free arm trajectories. The fruit-picking
throughput achieved by the algorithm was assessed using synthetically generated
fruit coordinates and a harvester design featuring up to 12 arms. The
throughput increased monotonically as more arms were added. Adding more arms
when fruit densities were low resulted in diminishing gains because it took
longer to travel from one fruit to another. However, when there were enough
fruits, the proposed algorithm achieved a linear speedup as the number of arms
increased. | Liked | jechoi@andrew.cmu.edu | Fast Heuristic Scheduling and Trajectory Planning for Robotic Fruit Harvesters with Multiple Cartesian Arms : This work proposes a fast heuristic algorithm for the coupled scheduling and
trajectory planning of multiple Cartesian robotic arms harvesting fruits. Our
method partitions the workspace, assigns fruit-picking sequences to arms,
determines tight and feasible fruit-picking schedules and vehicle travel speed,
and generates smooth, collision-free arm trajectories. The fruit-picking
throughput achieved by the algorithm was assessed using synthetically generated
fruit coordinates and a harvester design featuring up to 12 arms. The
throughput increased monotonically as more arms were added. Adding more arms
when fruit densities were low resulted in diminishing gains because it took
longer to travel from one fruit to another. However, when there were enough
fruits, the proposed algorithm achieved a linear speedup as the number of arms
increased. | 1 | jechoi@andrew.cmu.edu [SEP] Fast Heuristic Scheduling and Trajectory Planning for Robotic Fruit Harvesters with Multiple Cartesian Arms : This work proposes a fast heuristic algorithm for the coupled scheduling and
trajectory planning of multiple Cartesian robotic arms harvesting fruits. Our
method partitions the workspace, assigns fruit-picking sequences to arms,
determines tight and feasible fruit-picking schedules and vehicle travel speed,
and generates smooth, collision-free arm trajectories. The fruit-picking
throughput achieved by the algorithm was assessed using synthetically generated
fruit coordinates and a harvester design featuring up to 12 arms. The
throughput increased monotonically as more arms were added. Adding more arms
when fruit densities were low resulted in diminishing gains because it took
longer to travel from one fruit to another. However, when there were enough
fruits, the proposed algorithm achieved a linear speedup as the number of arms
increased. | 444 |
Quantum memristors for neuromorphic quantum machine learning | Quantum machine learning may permit to realize more efficient machine
learning calculations with near-term quantum devices. Among the diverse quantum
machine learning paradigms which are currently being considered, quantum
memristors are promising as a way of combining, in the same quantum hardware, a
unitary evolution with the nonlinearity provided by the measurement and
feedforward. Thus, an efficient way of deploying neuromorphic quantum computing
for quantum machine learning may be enabled. | Disliked | zrz@andrew.cmu.edu | Quantum memristors for neuromorphic quantum machine learning : Quantum machine learning may permit to realize more efficient machine
learning calculations with near-term quantum devices. Among the diverse quantum
machine learning paradigms which are currently being considered, quantum
memristors are promising as a way of combining, in the same quantum hardware, a
unitary evolution with the nonlinearity provided by the measurement and
feedforward. Thus, an efficient way of deploying neuromorphic quantum computing
for quantum machine learning may be enabled. | 0 | zrz@andrew.cmu.edu [SEP] Quantum memristors for neuromorphic quantum machine learning : Quantum machine learning may permit to realize more efficient machine
learning calculations with near-term quantum devices. Among the diverse quantum
machine learning paradigms which are currently being considered, quantum
memristors are promising as a way of combining, in the same quantum hardware, a
unitary evolution with the nonlinearity provided by the measurement and
feedforward. Thus, an efficient way of deploying neuromorphic quantum computing
for quantum machine learning may be enabled. | 63 |
Teaching Uncertainty Quantification in Machine Learning through Use Cases | Uncertainty in machine learning is not generally taught as general knowledge
in Machine Learning course curricula. In this paper we propose a short
curriculum for a course about uncertainty in machine learning, and complement
the course with a selection of use cases, aimed to trigger discussion and let
students play with the concepts of uncertainty in a programming setting. Our
use cases cover the concept of output uncertainty, Bayesian neural networks and
weight distributions, sources of uncertainty, and out of distribution
detection. We expect that this curriculum and set of use cases motivates the
community to adopt these important concepts into courses for safety in AI. | Disliked | zrz@andrew.cmu.edu | Teaching Uncertainty Quantification in Machine Learning through Use Cases : Uncertainty in machine learning is not generally taught as general knowledge
in Machine Learning course curricula. In this paper we propose a short
curriculum for a course about uncertainty in machine learning, and complement
the course with a selection of use cases, aimed to trigger discussion and let
students play with the concepts of uncertainty in a programming setting. Our
use cases cover the concept of output uncertainty, Bayesian neural networks and
weight distributions, sources of uncertainty, and out of distribution
detection. We expect that this curriculum and set of use cases motivates the
community to adopt these important concepts into courses for safety in AI. | 0 | zrz@andrew.cmu.edu [SEP] Teaching Uncertainty Quantification in Machine Learning through Use Cases : Uncertainty in machine learning is not generally taught as general knowledge
in Machine Learning course curricula. In this paper we propose a short
curriculum for a course about uncertainty in machine learning, and complement
the course with a selection of use cases, aimed to trigger discussion and let
students play with the concepts of uncertainty in a programming setting. Our
use cases cover the concept of output uncertainty, Bayesian neural networks and
weight distributions, sources of uncertainty, and out of distribution
detection. We expect that this curriculum and set of use cases motivates the
community to adopt these important concepts into courses for safety in AI. | 80 |
Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification | This paper investigates runtime monitoring of perception systems. Perception
is a critical component of high-integrity applications of robotics and
autonomous systems, such as self-driving cars. In these applications, failure
of perception systems may put human life at risk, and a broad adoption of these
technologies requires the development of methodologies to guarantee and monitor
safe operation. Despite the paramount importance of perception, currently there
is no formal approach for system-level perception monitoring. In this paper, we
formalize the problem of runtime fault detection and identification in
perception systems and present a framework to model diagnostic information
using a diagnostic graph. We then provide a set of deterministic,
probabilistic, and learning-based algorithms that use diagnostic graphs to
perform fault detection and identification. Moreover, we investigate
fundamental limits and provide deterministic and probabilistic guarantees on
the fault detection and identification results. We conclude the paper with an
extensive experimental evaluation, which recreates several realistic failure
modes in the LGSVL open-source autonomous driving simulator, and applies the
proposed system monitors to a state-of-the-art autonomous driving software
stack (Baidu's Apollo Auto). The results show that the proposed system monitors
outperform baselines, have the potential of preventing accidents in realistic
autonomous driving scenarios, and incur a negligible computational overhead. | Liked | zrz@andrew.cmu.edu | Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification : This paper investigates runtime monitoring of perception systems. Perception
is a critical component of high-integrity applications of robotics and
autonomous systems, such as self-driving cars. In these applications, failure
of perception systems may put human life at risk, and a broad adoption of these
technologies requires the development of methodologies to guarantee and monitor
safe operation. Despite the paramount importance of perception, currently there
is no formal approach for system-level perception monitoring. In this paper, we
formalize the problem of runtime fault detection and identification in
perception systems and present a framework to model diagnostic information
using a diagnostic graph. We then provide a set of deterministic,
probabilistic, and learning-based algorithms that use diagnostic graphs to
perform fault detection and identification. Moreover, we investigate
fundamental limits and provide deterministic and probabilistic guarantees on
the fault detection and identification results. We conclude the paper with an
extensive experimental evaluation, which recreates several realistic failure
modes in the LGSVL open-source autonomous driving simulator, and applies the
proposed system monitors to a state-of-the-art autonomous driving software
stack (Baidu's Apollo Auto). The results show that the proposed system monitors
outperform baselines, have the potential of preventing accidents in realistic
autonomous driving scenarios, and incur a negligible computational overhead. | 1 | zrz@andrew.cmu.edu [SEP] Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification : This paper investigates runtime monitoring of perception systems. Perception
is a critical component of high-integrity applications of robotics and
autonomous systems, such as self-driving cars. In these applications, failure
of perception systems may put human life at risk, and a broad adoption of these
technologies requires the development of methodologies to guarantee and monitor
safe operation. Despite the paramount importance of perception, currently there
is no formal approach for system-level perception monitoring. In this paper, we
formalize the problem of runtime fault detection and identification in
perception systems and present a framework to model diagnostic information
using a diagnostic graph. We then provide a set of deterministic,
probabilistic, and learning-based algorithms that use diagnostic graphs to
perform fault detection and identification. Moreover, we investigate
fundamental limits and provide deterministic and probabilistic guarantees on
the fault detection and identification results. We conclude the paper with an
extensive experimental evaluation, which recreates several realistic failure
modes in the LGSVL open-source autonomous driving simulator, and applies the
proposed system monitors to a state-of-the-art autonomous driving software
stack (Baidu's Apollo Auto). The results show that the proposed system monitors
outperform baselines, have the potential of preventing accidents in realistic
autonomous driving scenarios, and incur a negligible computational overhead. | 308 |
Visions in Theoretical Computer Science: A Report on the TCS Visioning Workshop 2020 | Theoretical computer science (TCS) is a subdiscipline of computer science
that studies the mathematical foundations of computational and algorithmic
processes and interactions. Work in this field is often recognized by its
emphasis on mathematical technique and rigor. At the heart of the field are
questions surrounding the nature of computation: What does it mean to compute?
What is computable? And how efficiently?
Every ten years or so the TCS community attends visioning workshops to
discuss the challenges and recent accomplishments in the TCS field. The
workshops and the outputs they produce are meant both as a reflection for the
TCS community and as guiding principles for interested investment partners.
Concretely, the workshop output consists of a number of nuggets, each
summarizing a particular point, that are synthesized in the form of a white
paper and illustrated with graphics/slides produced by a professional graphic
designer. The second TCS Visioning Workshop was organized by the SIGACT
Committee for the Advancement of Theoretical Computer Science and took place
during the week of July 20, 2020. Despite the conference being virtual, there
were over 76 participants, mostly from the United States, but also a few from
Europe and Asia who were able to attend due to the online format. Workshop
participants were divided into categories as reflected in the sections of this
report: (1) models of computation; (2) foundations of data science; (3)
cryptography; and (4) using theoretical computer science for other domains.
Each group participated in a series of discussions that produced the nuggets
below. | Disliked | zrz@andrew.cmu.edu | Visions in Theoretical Computer Science: A Report on the TCS Visioning Workshop 2020 : Theoretical computer science (TCS) is a subdiscipline of computer science
that studies the mathematical foundations of computational and algorithmic
processes and interactions. Work in this field is often recognized by its
emphasis on mathematical technique and rigor. At the heart of the field are
questions surrounding the nature of computation: What does it mean to compute?
What is computable? And how efficiently?
Every ten years or so the TCS community attends visioning workshops to
discuss the challenges and recent accomplishments in the TCS field. The
workshops and the outputs they produce are meant both as a reflection for the
TCS community and as guiding principles for interested investment partners.
Concretely, the workshop output consists of a number of nuggets, each
summarizing a particular point, that are synthesized in the form of a white
paper and illustrated with graphics/slides produced by a professional graphic
designer. The second TCS Visioning Workshop was organized by the SIGACT
Committee for the Advancement of Theoretical Computer Science and took place
during the week of July 20, 2020. Despite the conference being virtual, there
were over 76 participants, mostly from the United States, but also a few from
Europe and Asia who were able to attend due to the online format. Workshop
participants were divided into categories as reflected in the sections of this
report: (1) models of computation; (2) foundations of data science; (3)
cryptography; and (4) using theoretical computer science for other domains.
Each group participated in a series of discussions that produced the nuggets
below. | 0 | zrz@andrew.cmu.edu [SEP] Visions in Theoretical Computer Science: A Report on the TCS Visioning Workshop 2020 : Theoretical computer science (TCS) is a subdiscipline of computer science
that studies the mathematical foundations of computational and algorithmic
processes and interactions. Work in this field is often recognized by its
emphasis on mathematical technique and rigor. At the heart of the field are
questions surrounding the nature of computation: What does it mean to compute?
What is computable? And how efficiently?
Every ten years or so the TCS community attends visioning workshops to
discuss the challenges and recent accomplishments in the TCS field. The
workshops and the outputs they produce are meant both as a reflection for the
TCS community and as guiding principles for interested investment partners.
Concretely, the workshop output consists of a number of nuggets, each
summarizing a particular point, that are synthesized in the form of a white
paper and illustrated with graphics/slides produced by a professional graphic
designer. The second TCS Visioning Workshop was organized by the SIGACT
Committee for the Advancement of Theoretical Computer Science and took place
during the week of July 20, 2020. Despite the conference being virtual, there
were over 76 participants, mostly from the United States, but also a few from
Europe and Asia who were able to attend due to the online format. Workshop
participants were divided into categories as reflected in the sections of this
report: (1) models of computation; (2) foundations of data science; (3)
cryptography; and (4) using theoretical computer science for other domains.
Each group participated in a series of discussions that produced the nuggets
below. | 362 |
Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram | The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host
millions of pilgrims every year. During Hajj, the movement of large number of
people has a unique spatial and temporal constraints, which makes Hajj one of
toughest challenges for crowd management. In this paper, we propose a computer
vision based framework that automatically analyses video sequence and computes
important measurements which include estimation of crowd density,
identification of dominant patterns, detection and localization of congestion.
In addition, we analyze helpful statistics of the crowd like speed, and
direction, that could provide support to crowd management personnel. The
framework presented in this paper indicate that new advances in computer vision
and machine learning can be leveraged effectively for challenging and high
density crowd management applications. However, significant customization of
existing approaches is required to apply them to the challenging crowd
management situations in Masjid Al Haram. Our results paint a promising picture
for deployment of computer vision technologies to assist in quantitative
measurement of crowd size, density and congestion. | Disliked | zrz@andrew.cmu.edu | Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram : The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host
millions of pilgrims every year. During Hajj, the movement of large number of
people has a unique spatial and temporal constraints, which makes Hajj one of
toughest challenges for crowd management. In this paper, we propose a computer
vision based framework that automatically analyses video sequence and computes
important measurements which include estimation of crowd density,
identification of dominant patterns, detection and localization of congestion.
In addition, we analyze helpful statistics of the crowd like speed, and
direction, that could provide support to crowd management personnel. The
framework presented in this paper indicate that new advances in computer vision
and machine learning can be leveraged effectively for challenging and high
density crowd management applications. However, significant customization of
existing approaches is required to apply them to the challenging crowd
management situations in Masjid Al Haram. Our results paint a promising picture
for deployment of computer vision technologies to assist in quantitative
measurement of crowd size, density and congestion. | 0 | zrz@andrew.cmu.edu [SEP] Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram : The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host
millions of pilgrims every year. During Hajj, the movement of large number of
people has a unique spatial and temporal constraints, which makes Hajj one of
toughest challenges for crowd management. In this paper, we propose a computer
vision based framework that automatically analyses video sequence and computes
important measurements which include estimation of crowd density,
identification of dominant patterns, detection and localization of congestion.
In addition, we analyze helpful statistics of the crowd like speed, and
direction, that could provide support to crowd management personnel. The
framework presented in this paper indicate that new advances in computer vision
and machine learning can be leveraged effectively for challenging and high
density crowd management applications. However, significant customization of
existing approaches is required to apply them to the challenging crowd
management situations in Masjid Al Haram. Our results paint a promising picture
for deployment of computer vision technologies to assist in quantitative
measurement of crowd size, density and congestion. | 364 |
Joint Training of Deep Boltzmann Machines | We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks. | Liked | zrz@andrew.cmu.edu | Joint Training of Deep Boltzmann Machines : We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks. | 1 | zrz@andrew.cmu.edu [SEP] Joint Training of Deep Boltzmann Machines : We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks. | 198 |
Deep Causal Learning for Robotic Intelligence | This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence. | Disliked | zrz@andrew.cmu.edu | Deep Causal Learning for Robotic Intelligence : This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence. | 0 | zrz@andrew.cmu.edu [SEP] Deep Causal Learning for Robotic Intelligence : This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence. | 185 |
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review | Grid-centric perception is a crucial field for mobile robot perception and
navigation. Nonetheless, grid-centric perception is less prevalent than
object-centric perception as autonomous vehicles need to accurately perceive
highly dynamic, large-scale traffic scenarios and the complexity and
computational costs of grid-centric perception are high. In recent years, the
rapid development of deep learning techniques and hardware provides fresh
insights into the evolution of grid-centric perception. The fundamental
difference between grid-centric and object-centric pipeline lies in that
grid-centric perception follows a geometry-first paradigm which is more robust
to the open-world driving scenarios with endless long-tailed
semantically-unknown obstacles. Recent researches demonstrate the great
advantages of grid-centric perception, such as comprehensive fine-grained
environmental representation, greater robustness to occlusion and irregular
shaped objects, better ground estimation, and safer planning policies. There is
also a growing trend that the capacity of occupancy networks are greatly
expanded to 4D scene perception and prediction and latest techniques are highly
related to new research topics such as 4D occupancy forecasting, generative AI
and world models in the field of autonomous driving. Given the lack of current
surveys for this rapidly expanding field, we present a
hierarchically-structured review of grid-centric perception for autonomous
vehicles. We organize previous and current knowledge of occupancy grid
techniques along the main vein from 2D BEV grids to 3D occupancy to 4D
occupancy forecasting. We additionally summarize label-efficient occupancy
learning and the role of grid-centric perception in driving systems. Lastly, we
present a summary of the current research trend and provide future outlooks. | Liked | zrz@andrew.cmu.edu | Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review : Grid-centric perception is a crucial field for mobile robot perception and
navigation. Nonetheless, grid-centric perception is less prevalent than
object-centric perception as autonomous vehicles need to accurately perceive
highly dynamic, large-scale traffic scenarios and the complexity and
computational costs of grid-centric perception are high. In recent years, the
rapid development of deep learning techniques and hardware provides fresh
insights into the evolution of grid-centric perception. The fundamental
difference between grid-centric and object-centric pipeline lies in that
grid-centric perception follows a geometry-first paradigm which is more robust
to the open-world driving scenarios with endless long-tailed
semantically-unknown obstacles. Recent researches demonstrate the great
advantages of grid-centric perception, such as comprehensive fine-grained
environmental representation, greater robustness to occlusion and irregular
shaped objects, better ground estimation, and safer planning policies. There is
also a growing trend that the capacity of occupancy networks are greatly
expanded to 4D scene perception and prediction and latest techniques are highly
related to new research topics such as 4D occupancy forecasting, generative AI
and world models in the field of autonomous driving. Given the lack of current
surveys for this rapidly expanding field, we present a
hierarchically-structured review of grid-centric perception for autonomous
vehicles. We organize previous and current knowledge of occupancy grid
techniques along the main vein from 2D BEV grids to 3D occupancy to 4D
occupancy forecasting. We additionally summarize label-efficient occupancy
learning and the role of grid-centric perception in driving systems. Lastly, we
present a summary of the current research trend and provide future outlooks. | 1 | zrz@andrew.cmu.edu [SEP] Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review : Grid-centric perception is a crucial field for mobile robot perception and
navigation. Nonetheless, grid-centric perception is less prevalent than
object-centric perception as autonomous vehicles need to accurately perceive
highly dynamic, large-scale traffic scenarios and the complexity and
computational costs of grid-centric perception are high. In recent years, the
rapid development of deep learning techniques and hardware provides fresh
insights into the evolution of grid-centric perception. The fundamental
difference between grid-centric and object-centric pipeline lies in that
grid-centric perception follows a geometry-first paradigm which is more robust
to the open-world driving scenarios with endless long-tailed
semantically-unknown obstacles. Recent researches demonstrate the great
advantages of grid-centric perception, such as comprehensive fine-grained
environmental representation, greater robustness to occlusion and irregular
shaped objects, better ground estimation, and safer planning policies. There is
also a growing trend that the capacity of occupancy networks are greatly
expanded to 4D scene perception and prediction and latest techniques are highly
related to new research topics such as 4D occupancy forecasting, generative AI
and world models in the field of autonomous driving. Given the lack of current
surveys for this rapidly expanding field, we present a
hierarchically-structured review of grid-centric perception for autonomous
vehicles. We organize previous and current knowledge of occupancy grid
techniques along the main vein from 2D BEV grids to 3D occupancy to 4D
occupancy forecasting. We additionally summarize label-efficient occupancy
learning and the role of grid-centric perception in driving systems. Lastly, we
present a summary of the current research trend and provide future outlooks. | 306 |
Unknown Delay for Adversarial Bandit Setting with Multiple Play | This paper addresses the problem of unknown delays in adversarial multi-armed
bandit (MAB) with multiple play. Existing work on similar game setting focused
on only the case where the learner selects an arm in each round. However, there
are lots of applications in robotics where a learner needs to select more than
one arm per round. It is therefore worthwhile to investigate the effect of
delay when multiple arms are chosen. The multiple arms chosen per round in this
setting are such that they experience the same amount of delay. There can be an
aggregation of feedback losses from different combinations of arms selected at
different rounds, and the learner is faced with the challenge of associating
the feedback losses to the arms producing them. To address this problem, this
paper proposes a delayed exponential, exploitation and exploration for multiple
play (DEXP3.M) algorithm. The regret bound is only slightly worse than the
regret of DEXP3 already proposed for the single play setting with unknown
delay. | Liked | jechoi@andrew.cmu.edu | Unknown Delay for Adversarial Bandit Setting with Multiple Play : This paper addresses the problem of unknown delays in adversarial multi-armed
bandit (MAB) with multiple play. Existing work on similar game setting focused
on only the case where the learner selects an arm in each round. However, there
are lots of applications in robotics where a learner needs to select more than
one arm per round. It is therefore worthwhile to investigate the effect of
delay when multiple arms are chosen. The multiple arms chosen per round in this
setting are such that they experience the same amount of delay. There can be an
aggregation of feedback losses from different combinations of arms selected at
different rounds, and the learner is faced with the challenge of associating
the feedback losses to the arms producing them. To address this problem, this
paper proposes a delayed exponential, exploitation and exploration for multiple
play (DEXP3.M) algorithm. The regret bound is only slightly worse than the
regret of DEXP3 already proposed for the single play setting with unknown
delay. | 1 | jechoi@andrew.cmu.edu [SEP] Unknown Delay for Adversarial Bandit Setting with Multiple Play : This paper addresses the problem of unknown delays in adversarial multi-armed
bandit (MAB) with multiple play. Existing work on similar game setting focused
on only the case where the learner selects an arm in each round. However, there
are lots of applications in robotics where a learner needs to select more than
one arm per round. It is therefore worthwhile to investigate the effect of
delay when multiple arms are chosen. The multiple arms chosen per round in this
setting are such that they experience the same amount of delay. There can be an
aggregation of feedback losses from different combinations of arms selected at
different rounds, and the learner is faced with the challenge of associating
the feedback losses to the arms producing them. To address this problem, this
paper proposes a delayed exponential, exploitation and exploration for multiple
play (DEXP3.M) algorithm. The regret bound is only slightly worse than the
regret of DEXP3 already proposed for the single play setting with unknown
delay. | 557 |
Design and Implementation of a DTMF Based Pick and Place Robotic Arm | In recent times, developments in field of communication and robotics has
progressed with leaps and bounds. In addition, the blend of both disciplines
has contributed heavily in making human life easier and better. So in this work
while making use of both the aforementioned technologies, a procedure for
design and implementation of a mobile operated mechanical arm is proposed, that
is, the proposed arm will be operated via a cellular device that connects with
the receiver mounted on the robotic arm. Moreover, over the duration of a call,
if any key is pressed from the cellular device than an indicator indistinct to
the key pressed is noticed at the receiver side. This tone represents
superimposition of two distinct frequencies and referred to as DTMF (dual tone
multi-frequency). Further, the mechanical arm is handled via the DTMF tone.
Also, the acquired tone at the receiver is taken into a micro-controller
(ATMEGA16) using the DTMF decipher module i.e. MT8870. Further, the decipher
module unwinds the DTMF signal into its corresponding two bit representation
and then the matched number is transmitted to the micro-controller. The
micro-controller is programmed to take an action based on the decoded value.
Further, the micro-controller forwards control signals to the motor driver unit
to move the arm in forward/backward or multi-directional course. Lastly, the
mechanical arm is capable of picking and placing objects while being controlled
wirelessly over GSM (Global System for Mobile Communications). | Liked | jechoi@andrew.cmu.edu | Design and Implementation of a DTMF Based Pick and Place Robotic Arm : In recent times, developments in field of communication and robotics has
progressed with leaps and bounds. In addition, the blend of both disciplines
has contributed heavily in making human life easier and better. So in this work
while making use of both the aforementioned technologies, a procedure for
design and implementation of a mobile operated mechanical arm is proposed, that
is, the proposed arm will be operated via a cellular device that connects with
the receiver mounted on the robotic arm. Moreover, over the duration of a call,
if any key is pressed from the cellular device than an indicator indistinct to
the key pressed is noticed at the receiver side. This tone represents
superimposition of two distinct frequencies and referred to as DTMF (dual tone
multi-frequency). Further, the mechanical arm is handled via the DTMF tone.
Also, the acquired tone at the receiver is taken into a micro-controller
(ATMEGA16) using the DTMF decipher module i.e. MT8870. Further, the decipher
module unwinds the DTMF signal into its corresponding two bit representation
and then the matched number is transmitted to the micro-controller. The
micro-controller is programmed to take an action based on the decoded value.
Further, the micro-controller forwards control signals to the motor driver unit
to move the arm in forward/backward or multi-directional course. Lastly, the
mechanical arm is capable of picking and placing objects while being controlled
wirelessly over GSM (Global System for Mobile Communications). | 1 | jechoi@andrew.cmu.edu [SEP] Design and Implementation of a DTMF Based Pick and Place Robotic Arm : In recent times, developments in field of communication and robotics has
progressed with leaps and bounds. In addition, the blend of both disciplines
has contributed heavily in making human life easier and better. So in this work
while making use of both the aforementioned technologies, a procedure for
design and implementation of a mobile operated mechanical arm is proposed, that
is, the proposed arm will be operated via a cellular device that connects with
the receiver mounted on the robotic arm. Moreover, over the duration of a call,
if any key is pressed from the cellular device than an indicator indistinct to
the key pressed is noticed at the receiver side. This tone represents
superimposition of two distinct frequencies and referred to as DTMF (dual tone
multi-frequency). Further, the mechanical arm is handled via the DTMF tone.
Also, the acquired tone at the receiver is taken into a micro-controller
(ATMEGA16) using the DTMF decipher module i.e. MT8870. Further, the decipher
module unwinds the DTMF signal into its corresponding two bit representation
and then the matched number is transmitted to the micro-controller. The
micro-controller is programmed to take an action based on the decoded value.
Further, the micro-controller forwards control signals to the motor driver unit
to move the arm in forward/backward or multi-directional course. Lastly, the
mechanical arm is capable of picking and placing objects while being controlled
wirelessly over GSM (Global System for Mobile Communications). | 547 |
TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning | TF.Learn is a high-level Python module for distributed machine learning
inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to
simplify the process of creating, configuring, training, evaluating, and
experimenting a machine learning model. TF.Learn integrates a wide range of
state-of-art machine learning algorithms built on top of TensorFlow's low level
APIs for small to large-scale supervised and unsupervised problems. This module
focuses on bringing machine learning to non-specialists using a general-purpose
high-level language as well as researchers who want to implement, benchmark,
and compare their new methods in a structured environment. Emphasis is put on
ease of use, performance, documentation, and API consistency. | Liked | zrz@andrew.cmu.edu | TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning : TF.Learn is a high-level Python module for distributed machine learning
inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to
simplify the process of creating, configuring, training, evaluating, and
experimenting a machine learning model. TF.Learn integrates a wide range of
state-of-art machine learning algorithms built on top of TensorFlow's low level
APIs for small to large-scale supervised and unsupervised problems. This module
focuses on bringing machine learning to non-specialists using a general-purpose
high-level language as well as researchers who want to implement, benchmark,
and compare their new methods in a structured environment. Emphasis is put on
ease of use, performance, documentation, and API consistency. | 1 | zrz@andrew.cmu.edu [SEP] TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning : TF.Learn is a high-level Python module for distributed machine learning
inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to
simplify the process of creating, configuring, training, evaluating, and
experimenting a machine learning model. TF.Learn integrates a wide range of
state-of-art machine learning algorithms built on top of TensorFlow's low level
APIs for small to large-scale supervised and unsupervised problems. This module
focuses on bringing machine learning to non-specialists using a general-purpose
high-level language as well as researchers who want to implement, benchmark,
and compare their new methods in a structured environment. Emphasis is put on
ease of use, performance, documentation, and API consistency. | 98 |
Computer Stereo Vision for Autonomous Driving | As an important component of autonomous systems, autonomous car perception
has had a big leap with recent advances in parallel computing architectures.
With the use of tiny but full-feature embedded supercomputers, computer stereo
vision has been prevalently applied in autonomous cars for depth perception.
The two key aspects of computer stereo vision are speed and accuracy. They are
both desirable but conflicting properties, as the algorithms with better
disparity accuracy usually have higher computational complexity. Therefore, the
main aim of developing a computer stereo vision algorithm for resource-limited
hardware is to improve the trade-off between speed and accuracy. In this
chapter, we introduce both the hardware and software aspects of computer stereo
vision for autonomous car systems. Then, we discuss four autonomous car
perception tasks, including 1) visual feature detection, description and
matching, 2) 3D information acquisition, 3) object detection/recognition and 4)
semantic image segmentation. The principles of computer stereo vision and
parallel computing on multi-threading CPU and GPU architectures are then
detailed. | Liked | zrz@andrew.cmu.edu | Computer Stereo Vision for Autonomous Driving : As an important component of autonomous systems, autonomous car perception
has had a big leap with recent advances in parallel computing architectures.
With the use of tiny but full-feature embedded supercomputers, computer stereo
vision has been prevalently applied in autonomous cars for depth perception.
The two key aspects of computer stereo vision are speed and accuracy. They are
both desirable but conflicting properties, as the algorithms with better
disparity accuracy usually have higher computational complexity. Therefore, the
main aim of developing a computer stereo vision algorithm for resource-limited
hardware is to improve the trade-off between speed and accuracy. In this
chapter, we introduce both the hardware and software aspects of computer stereo
vision for autonomous car systems. Then, we discuss four autonomous car
perception tasks, including 1) visual feature detection, description and
matching, 2) 3D information acquisition, 3) object detection/recognition and 4)
semantic image segmentation. The principles of computer stereo vision and
parallel computing on multi-threading CPU and GPU architectures are then
detailed. | 1 | zrz@andrew.cmu.edu [SEP] Computer Stereo Vision for Autonomous Driving : As an important component of autonomous systems, autonomous car perception
has had a big leap with recent advances in parallel computing architectures.
With the use of tiny but full-feature embedded supercomputers, computer stereo
vision has been prevalently applied in autonomous cars for depth perception.
The two key aspects of computer stereo vision are speed and accuracy. They are
both desirable but conflicting properties, as the algorithms with better
disparity accuracy usually have higher computational complexity. Therefore, the
main aim of developing a computer stereo vision algorithm for resource-limited
hardware is to improve the trade-off between speed and accuracy. In this
chapter, we introduce both the hardware and software aspects of computer stereo
vision for autonomous car systems. Then, we discuss four autonomous car
perception tasks, including 1) visual feature detection, description and
matching, 2) 3D information acquisition, 3) object detection/recognition and 4)
semantic image segmentation. The principles of computer stereo vision and
parallel computing on multi-threading CPU and GPU architectures are then
detailed. | 313 |
Bayesian Optimization for Machine Learning : A Practical Guidebook | The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniques. We outline four example machine learning problems that
can be solved using open source machine learning libraries, and highlight the
benefits of using Bayesian optimization in the context of these common machine
learning applications. | Liked | zrz@andrew.cmu.edu | Bayesian Optimization for Machine Learning : A Practical Guidebook : The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniques. We outline four example machine learning problems that
can be solved using open source machine learning libraries, and highlight the
benefits of using Bayesian optimization in the context of these common machine
learning applications. | 1 | zrz@andrew.cmu.edu [SEP] Bayesian Optimization for Machine Learning : A Practical Guidebook : The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniques. We outline four example machine learning problems that
can be solved using open source machine learning libraries, and highlight the
benefits of using Bayesian optimization in the context of these common machine
learning applications. | 69 |
MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data? | We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task, users only specify what machine learning task to run and the
cloud figures out the rest. Raising the level of abstraction, however, rarely
comes free - a performance penalty is possible. How good, then, are current
machine learning clouds on real-world machine learning workloads?
We study this question with a focus on binary classication problems. We
present mlbench, a novel benchmark constructed by harvesting datasets from
Kaggle competitions. We then compare the performance of the top winning code
available from Kaggle with that of running machine learning clouds from both
Azure and Amazon on mlbench. Our comparative study reveals the strength and
weakness of existing machine learning clouds and points out potential future
directions for improvement. | Liked | zrz@andrew.cmu.edu | MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data? : We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task, users only specify what machine learning task to run and the
cloud figures out the rest. Raising the level of abstraction, however, rarely
comes free - a performance penalty is possible. How good, then, are current
machine learning clouds on real-world machine learning workloads?
We study this question with a focus on binary classication problems. We
present mlbench, a novel benchmark constructed by harvesting datasets from
Kaggle competitions. We then compare the performance of the top winning code
available from Kaggle with that of running machine learning clouds from both
Azure and Amazon on mlbench. Our comparative study reveals the strength and
weakness of existing machine learning clouds and points out potential future
directions for improvement. | 1 | zrz@andrew.cmu.edu [SEP] MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data? : We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task, users only specify what machine learning task to run and the
cloud figures out the rest. Raising the level of abstraction, however, rarely
comes free - a performance penalty is possible. How good, then, are current
machine learning clouds on real-world machine learning workloads?
We study this question with a focus on binary classication problems. We
present mlbench, a novel benchmark constructed by harvesting datasets from
Kaggle competitions. We then compare the performance of the top winning code
available from Kaggle with that of running machine learning clouds from both
Azure and Amazon on mlbench. Our comparative study reveals the strength and
weakness of existing machine learning clouds and points out potential future
directions for improvement. | 32 |
Human Arm Pose Estimation with a Shoulder-worn Force-Myography Device for Human-Robot Interaction | Accurate human pose estimation is essential for effective Human-Robot
Interaction (HRI). By observing a user's arm movements, robots can respond
appropriately, whether it's providing assistance or avoiding collisions. While
visual perception offers potential for human pose estimation, it can be
hindered by factors like poor lighting or occlusions. Additionally, wearable
inertial sensors, though useful, require frequent calibration as they do not
provide absolute position information. Force-myography (FMG) is an alternative
approach where muscle perturbations are externally measured. It has been used
to observe finger movements, but its application to full arm state estimation
is unexplored. In this letter, we investigate the use of a wearable FMG device
that can observe the state of the human arm for real-time applications of HRI.
We propose a Transformer-based model to map FMG measurements from the shoulder
of the user to the physical pose of the arm. The model is also shown to be
transferable to other users with limited decline in accuracy. Through
real-world experiments with a robotic arm, we demonstrate collision avoidance
without relying on visual perception. | Liked | jechoi@andrew.cmu.edu | Human Arm Pose Estimation with a Shoulder-worn Force-Myography Device for Human-Robot Interaction : Accurate human pose estimation is essential for effective Human-Robot
Interaction (HRI). By observing a user's arm movements, robots can respond
appropriately, whether it's providing assistance or avoiding collisions. While
visual perception offers potential for human pose estimation, it can be
hindered by factors like poor lighting or occlusions. Additionally, wearable
inertial sensors, though useful, require frequent calibration as they do not
provide absolute position information. Force-myography (FMG) is an alternative
approach where muscle perturbations are externally measured. It has been used
to observe finger movements, but its application to full arm state estimation
is unexplored. In this letter, we investigate the use of a wearable FMG device
that can observe the state of the human arm for real-time applications of HRI.
We propose a Transformer-based model to map FMG measurements from the shoulder
of the user to the physical pose of the arm. The model is also shown to be
transferable to other users with limited decline in accuracy. Through
real-world experiments with a robotic arm, we demonstrate collision avoidance
without relying on visual perception. | 1 | jechoi@andrew.cmu.edu [SEP] Human Arm Pose Estimation with a Shoulder-worn Force-Myography Device for Human-Robot Interaction : Accurate human pose estimation is essential for effective Human-Robot
Interaction (HRI). By observing a user's arm movements, robots can respond
appropriately, whether it's providing assistance or avoiding collisions. While
visual perception offers potential for human pose estimation, it can be
hindered by factors like poor lighting or occlusions. Additionally, wearable
inertial sensors, though useful, require frequent calibration as they do not
provide absolute position information. Force-myography (FMG) is an alternative
approach where muscle perturbations are externally measured. It has been used
to observe finger movements, but its application to full arm state estimation
is unexplored. In this letter, we investigate the use of a wearable FMG device
that can observe the state of the human arm for real-time applications of HRI.
We propose a Transformer-based model to map FMG measurements from the shoulder
of the user to the physical pose of the arm. The model is also shown to be
transferable to other users with limited decline in accuracy. Through
real-world experiments with a robotic arm, we demonstrate collision avoidance
without relying on visual perception. | 437 |
Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics | We introduce Matched Machine Learning, a framework that combines the
flexibility of machine learning black boxes with the interpretability of
matching, a longstanding tool in observational causal inference.
Interpretability is paramount in many high-stakes application of causal
inference. Current tools for nonparametric estimation of both average and
individualized treatment effects are black-boxes that do not allow for human
auditing of estimates. Our framework uses machine learning to learn an optimal
metric for matching units and estimating outcomes, thus achieving the
performance of machine learning black-boxes, while being interpretable. Our
general framework encompasses several published works as special cases. We
provide asymptotic inference theory for our proposed framework, enabling users
to construct approximate confidence intervals around estimates of both
individualized and average treatment effects. We show empirically that
instances of Matched Machine Learning perform on par with black-box machine
learning methods and better than existing matching methods for similar
problems. Finally, in our application we show how Matched Machine Learning can
be used to perform causal inference even when covariate data are highly
complex: we study an image dataset, and produce high quality matches and
estimates of treatment effects. | Disliked | zrz@andrew.cmu.edu | Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics : We introduce Matched Machine Learning, a framework that combines the
flexibility of machine learning black boxes with the interpretability of
matching, a longstanding tool in observational causal inference.
Interpretability is paramount in many high-stakes application of causal
inference. Current tools for nonparametric estimation of both average and
individualized treatment effects are black-boxes that do not allow for human
auditing of estimates. Our framework uses machine learning to learn an optimal
metric for matching units and estimating outcomes, thus achieving the
performance of machine learning black-boxes, while being interpretable. Our
general framework encompasses several published works as special cases. We
provide asymptotic inference theory for our proposed framework, enabling users
to construct approximate confidence intervals around estimates of both
individualized and average treatment effects. We show empirically that
instances of Matched Machine Learning perform on par with black-box machine
learning methods and better than existing matching methods for similar
problems. Finally, in our application we show how Matched Machine Learning can
be used to perform causal inference even when covariate data are highly
complex: we study an image dataset, and produce high quality matches and
estimates of treatment effects. | 0 | zrz@andrew.cmu.edu [SEP] Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics : We introduce Matched Machine Learning, a framework that combines the
flexibility of machine learning black boxes with the interpretability of
matching, a longstanding tool in observational causal inference.
Interpretability is paramount in many high-stakes application of causal
inference. Current tools for nonparametric estimation of both average and
individualized treatment effects are black-boxes that do not allow for human
auditing of estimates. Our framework uses machine learning to learn an optimal
metric for matching units and estimating outcomes, thus achieving the
performance of machine learning black-boxes, while being interpretable. Our
general framework encompasses several published works as special cases. We
provide asymptotic inference theory for our proposed framework, enabling users
to construct approximate confidence intervals around estimates of both
individualized and average treatment effects. We show empirically that
instances of Matched Machine Learning perform on par with black-box machine
learning methods and better than existing matching methods for similar
problems. Finally, in our application we show how Matched Machine Learning can
be used to perform causal inference even when covariate data are highly
complex: we study an image dataset, and produce high quality matches and
estimates of treatment effects. | 129 |
WiCV 2021: The Eighth Women In Computer Vision Workshop | In this paper, we present the details of Women in Computer Vision Workshop -
WiCV 2021, organized alongside the virtual CVPR 2021. It provides a voice to a
minority (female) group in the computer vision community and focuses on
increasing the visibility of these researchers, both in academia and industry.
WiCV believes that such an event can play an important role in lowering the
gender imbalance in the field of computer vision. WiCV is organized each year
where it provides a)~opportunity for collaboration between researchers from
minority groups, b)~mentorship to female junior researchers, c)~financial
support to presenters to overcome monetary burden and d)~large and diverse
choice of role models, who can serve as examples to younger researchers at the
beginning of their careers. In this paper, we present a report on the workshop
program, trends over the past years, a summary of statistics regarding
presenters, attendees, and sponsorship for the WiCV 2021 workshop. | Disliked | zrz@andrew.cmu.edu | WiCV 2021: The Eighth Women In Computer Vision Workshop : In this paper, we present the details of Women in Computer Vision Workshop -
WiCV 2021, organized alongside the virtual CVPR 2021. It provides a voice to a
minority (female) group in the computer vision community and focuses on
increasing the visibility of these researchers, both in academia and industry.
WiCV believes that such an event can play an important role in lowering the
gender imbalance in the field of computer vision. WiCV is organized each year
where it provides a)~opportunity for collaboration between researchers from
minority groups, b)~mentorship to female junior researchers, c)~financial
support to presenters to overcome monetary burden and d)~large and diverse
choice of role models, who can serve as examples to younger researchers at the
beginning of their careers. In this paper, we present a report on the workshop
program, trends over the past years, a summary of statistics regarding
presenters, attendees, and sponsorship for the WiCV 2021 workshop. | 0 | zrz@andrew.cmu.edu [SEP] WiCV 2021: The Eighth Women In Computer Vision Workshop : In this paper, we present the details of Women in Computer Vision Workshop -
WiCV 2021, organized alongside the virtual CVPR 2021. It provides a voice to a
minority (female) group in the computer vision community and focuses on
increasing the visibility of these researchers, both in academia and industry.
WiCV believes that such an event can play an important role in lowering the
gender imbalance in the field of computer vision. WiCV is organized each year
where it provides a)~opportunity for collaboration between researchers from
minority groups, b)~mentorship to female junior researchers, c)~financial
support to presenters to overcome monetary burden and d)~large and diverse
choice of role models, who can serve as examples to younger researchers at the
beginning of their careers. In this paper, we present a report on the workshop
program, trends over the past years, a summary of statistics regarding
presenters, attendees, and sponsorship for the WiCV 2021 workshop. | 371 |
Learning to Centralize Dual-Arm Assembly | Robotic manipulators are widely used in modern manufacturing processes.
However, their deployment in unstructured environments remains an open problem.
To deal with the variety, complexity, and uncertainty of real-world
manipulation tasks, it is essential to develop a flexible framework with
reduced assumptions on the environment characteristics. In recent years,
reinforcement learning (RL) has shown great results for single-arm robotic
manipulation. However, research focusing on dual-arm manipulation is still
rare. From a classical control perspective, solving such tasks often involves
complex modeling of interactions between two manipulators and the objects
encountered in the tasks, as well as the two robots coupling at a control
level. Instead, in this work, we explore the applicability of model-free RL to
dual-arm assembly. As we aim to contribute towards an approach that is not
limited to dual-arm assembly, but dual-arm manipulation in general, we keep
modeling efforts at a minimum. Hence, to avoid modeling the interaction between
the two robots and the used assembly tools, we present a modular approach with
two decentralized single-arm controllers which are coupled using a single
centralized learned policy. We reduce modeling effort to a minimum by using
sparse rewards only. Our architecture enables successful assembly and simple
transfer from simulation to the real world. We demonstrate the effectiveness of
the framework on dual-arm peg-in-hole and analyze sample efficiency and success
rates for different action spaces. Moreover, we compare results on different
clearances and showcase disturbance recovery and robustness, when dealing with
position uncertainties. Finally we zero-shot transfer policies trained in
simulation to the real world and evaluate their performance. | Liked | jechoi@andrew.cmu.edu | Learning to Centralize Dual-Arm Assembly : Robotic manipulators are widely used in modern manufacturing processes.
However, their deployment in unstructured environments remains an open problem.
To deal with the variety, complexity, and uncertainty of real-world
manipulation tasks, it is essential to develop a flexible framework with
reduced assumptions on the environment characteristics. In recent years,
reinforcement learning (RL) has shown great results for single-arm robotic
manipulation. However, research focusing on dual-arm manipulation is still
rare. From a classical control perspective, solving such tasks often involves
complex modeling of interactions between two manipulators and the objects
encountered in the tasks, as well as the two robots coupling at a control
level. Instead, in this work, we explore the applicability of model-free RL to
dual-arm assembly. As we aim to contribute towards an approach that is not
limited to dual-arm assembly, but dual-arm manipulation in general, we keep
modeling efforts at a minimum. Hence, to avoid modeling the interaction between
the two robots and the used assembly tools, we present a modular approach with
two decentralized single-arm controllers which are coupled using a single
centralized learned policy. We reduce modeling effort to a minimum by using
sparse rewards only. Our architecture enables successful assembly and simple
transfer from simulation to the real world. We demonstrate the effectiveness of
the framework on dual-arm peg-in-hole and analyze sample efficiency and success
rates for different action spaces. Moreover, we compare results on different
clearances and showcase disturbance recovery and robustness, when dealing with
position uncertainties. Finally we zero-shot transfer policies trained in
simulation to the real world and evaluate their performance. | 1 | jechoi@andrew.cmu.edu [SEP] Learning to Centralize Dual-Arm Assembly : Robotic manipulators are widely used in modern manufacturing processes.
However, their deployment in unstructured environments remains an open problem.
To deal with the variety, complexity, and uncertainty of real-world
manipulation tasks, it is essential to develop a flexible framework with
reduced assumptions on the environment characteristics. In recent years,
reinforcement learning (RL) has shown great results for single-arm robotic
manipulation. However, research focusing on dual-arm manipulation is still
rare. From a classical control perspective, solving such tasks often involves
complex modeling of interactions between two manipulators and the objects
encountered in the tasks, as well as the two robots coupling at a control
level. Instead, in this work, we explore the applicability of model-free RL to
dual-arm assembly. As we aim to contribute towards an approach that is not
limited to dual-arm assembly, but dual-arm manipulation in general, we keep
modeling efforts at a minimum. Hence, to avoid modeling the interaction between
the two robots and the used assembly tools, we present a modular approach with
two decentralized single-arm controllers which are coupled using a single
centralized learned policy. We reduce modeling effort to a minimum by using
sparse rewards only. Our architecture enables successful assembly and simple
transfer from simulation to the real world. We demonstrate the effectiveness of
the framework on dual-arm peg-in-hole and analyze sample efficiency and success
rates for different action spaces. Moreover, we compare results on different
clearances and showcase disturbance recovery and robustness, when dealing with
position uncertainties. Finally we zero-shot transfer policies trained in
simulation to the real world and evaluate their performance. | 477 |
A Survey of Optimization Methods from a Machine Learning Perspective | Machine learning develops rapidly, which has made many theoretical
breakthroughs and is widely applied in various fields. Optimization, as an
important part of machine learning, has attracted much attention of
researchers. With the exponential growth of data amount and the increase of
model complexity, optimization methods in machine learning face more and more
challenges. A lot of work on solving optimization problems or improving
optimization methods in machine learning has been proposed successively. The
systematic retrospect and summary of the optimization methods from the
perspective of machine learning are of great significance, which can offer
guidance for both developments of optimization and machine learning research.
In this paper, we first describe the optimization problems in machine learning.
Then, we introduce the principles and progresses of commonly used optimization
methods. Next, we summarize the applications and developments of optimization
methods in some popular machine learning fields. Finally, we explore and give
some challenges and open problems for the optimization in machine learning. | Liked | zrz@andrew.cmu.edu | A Survey of Optimization Methods from a Machine Learning Perspective : Machine learning develops rapidly, which has made many theoretical
breakthroughs and is widely applied in various fields. Optimization, as an
important part of machine learning, has attracted much attention of
researchers. With the exponential growth of data amount and the increase of
model complexity, optimization methods in machine learning face more and more
challenges. A lot of work on solving optimization problems or improving
optimization methods in machine learning has been proposed successively. The
systematic retrospect and summary of the optimization methods from the
perspective of machine learning are of great significance, which can offer
guidance for both developments of optimization and machine learning research.
In this paper, we first describe the optimization problems in machine learning.
Then, we introduce the principles and progresses of commonly used optimization
methods. Next, we summarize the applications and developments of optimization
methods in some popular machine learning fields. Finally, we explore and give
some challenges and open problems for the optimization in machine learning. | 1 | zrz@andrew.cmu.edu [SEP] A Survey of Optimization Methods from a Machine Learning Perspective : Machine learning develops rapidly, which has made many theoretical
breakthroughs and is widely applied in various fields. Optimization, as an
important part of machine learning, has attracted much attention of
researchers. With the exponential growth of data amount and the increase of
model complexity, optimization methods in machine learning face more and more
challenges. A lot of work on solving optimization problems or improving
optimization methods in machine learning has been proposed successively. The
systematic retrospect and summary of the optimization methods from the
perspective of machine learning are of great significance, which can offer
guidance for both developments of optimization and machine learning research.
In this paper, we first describe the optimization problems in machine learning.
Then, we introduce the principles and progresses of commonly used optimization
methods. Next, we summarize the applications and developments of optimization
methods in some popular machine learning fields. Finally, we explore and give
some challenges and open problems for the optimization in machine learning. | 42 |
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving | In the field of autonomous driving, two important features of autonomous
driving car systems are the explainability of decision logic and the accuracy
of environmental perception. This paper introduces DME-Driver, a new autonomous
driving system that enhances the performance and reliability of autonomous
driving system. DME-Driver utilizes a powerful vision language model as the
decision-maker and a planning-oriented perception model as the control signal
generator. To ensure explainable and reliable driving decisions, the logical
decision-maker is constructed based on a large vision language model. This
model follows the logic employed by experienced human drivers and makes
decisions in a similar manner. On the other hand, the generation of accurate
control signals relies on precise and detailed environmental perception, which
is where 3D scene perception models excel. Therefore, a planning oriented
perception model is employed as the signal generator. It translates the logical
decisions made by the decision-maker into accurate control signals for the
self-driving cars. To effectively train the proposed model, a new dataset for
autonomous driving was created. This dataset encompasses a diverse range of
human driver behaviors and their underlying motivations. By leveraging this
dataset, our model achieves high-precision planning accuracy through a logical
thinking process. | Liked | zrz@andrew.cmu.edu | DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving : In the field of autonomous driving, two important features of autonomous
driving car systems are the explainability of decision logic and the accuracy
of environmental perception. This paper introduces DME-Driver, a new autonomous
driving system that enhances the performance and reliability of autonomous
driving system. DME-Driver utilizes a powerful vision language model as the
decision-maker and a planning-oriented perception model as the control signal
generator. To ensure explainable and reliable driving decisions, the logical
decision-maker is constructed based on a large vision language model. This
model follows the logic employed by experienced human drivers and makes
decisions in a similar manner. On the other hand, the generation of accurate
control signals relies on precise and detailed environmental perception, which
is where 3D scene perception models excel. Therefore, a planning oriented
perception model is employed as the signal generator. It translates the logical
decisions made by the decision-maker into accurate control signals for the
self-driving cars. To effectively train the proposed model, a new dataset for
autonomous driving was created. This dataset encompasses a diverse range of
human driver behaviors and their underlying motivations. By leveraging this
dataset, our model achieves high-precision planning accuracy through a logical
thinking process. | 1 | zrz@andrew.cmu.edu [SEP] DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving : In the field of autonomous driving, two important features of autonomous
driving car systems are the explainability of decision logic and the accuracy
of environmental perception. This paper introduces DME-Driver, a new autonomous
driving system that enhances the performance and reliability of autonomous
driving system. DME-Driver utilizes a powerful vision language model as the
decision-maker and a planning-oriented perception model as the control signal
generator. To ensure explainable and reliable driving decisions, the logical
decision-maker is constructed based on a large vision language model. This
model follows the logic employed by experienced human drivers and makes
decisions in a similar manner. On the other hand, the generation of accurate
control signals relies on precise and detailed environmental perception, which
is where 3D scene perception models excel. Therefore, a planning oriented
perception model is employed as the signal generator. It translates the logical
decisions made by the decision-maker into accurate control signals for the
self-driving cars. To effectively train the proposed model, a new dataset for
autonomous driving was created. This dataset encompasses a diverse range of
human driver behaviors and their underlying motivations. By leveraging this
dataset, our model achieves high-precision planning accuracy through a logical
thinking process. | 295 |
Financial Time Series Data Processing for Machine Learning | This article studies the financial time series data processing for machine
learning. It introduces the most frequent scaling methods, then compares the
resulting stationarity and preservation of useful information for trend
forecasting. It proposes an empirical test based on the capability to learn
simple data relationship with simple models. It also speaks about the data
split method specific to time series, avoiding unwanted overfitting and
proposes various labelling for classification and regression. | Liked | zrz@andrew.cmu.edu | Financial Time Series Data Processing for Machine Learning : This article studies the financial time series data processing for machine
learning. It introduces the most frequent scaling methods, then compares the
resulting stationarity and preservation of useful information for trend
forecasting. It proposes an empirical test based on the capability to learn
simple data relationship with simple models. It also speaks about the data
split method specific to time series, avoiding unwanted overfitting and
proposes various labelling for classification and regression. | 1 | zrz@andrew.cmu.edu [SEP] Financial Time Series Data Processing for Machine Learning : This article studies the financial time series data processing for machine
learning. It introduces the most frequent scaling methods, then compares the
resulting stationarity and preservation of useful information for trend
forecasting. It proposes an empirical test based on the capability to learn
simple data relationship with simple models. It also speaks about the data
split method specific to time series, avoiding unwanted overfitting and
proposes various labelling for classification and regression. | 120 |
Combining Deep Learning with Good Old-Fashioned Machine Learning | We present a comprehensive, stacking-based framework for combining deep
learning with good old-fashioned machine learning, called Deep GOld. Our
framework involves ensemble selection from 51 retrained pretrained deep
networks as first-level models, and 10 machine-learning algorithms as
second-level models. Enabled by today's state-of-the-art software tools and
hardware platforms, Deep GOld delivers consistent improvement when tested on
four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny
ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original
networks' performance. | Liked | zrz@andrew.cmu.edu | Combining Deep Learning with Good Old-Fashioned Machine Learning : We present a comprehensive, stacking-based framework for combining deep
learning with good old-fashioned machine learning, called Deep GOld. Our
framework involves ensemble selection from 51 retrained pretrained deep
networks as first-level models, and 10 machine-learning algorithms as
second-level models. Enabled by today's state-of-the-art software tools and
hardware platforms, Deep GOld delivers consistent improvement when tested on
four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny
ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original
networks' performance. | 1 | zrz@andrew.cmu.edu [SEP] Combining Deep Learning with Good Old-Fashioned Machine Learning : We present a comprehensive, stacking-based framework for combining deep
learning with good old-fashioned machine learning, called Deep GOld. Our
framework involves ensemble selection from 51 retrained pretrained deep
networks as first-level models, and 10 machine-learning algorithms as
second-level models. Enabled by today's state-of-the-art software tools and
hardware platforms, Deep GOld delivers consistent improvement when tested on
four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny
ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original
networks' performance. | 193 |
Machine Learning for Clinical Predictive Analytics | In this chapter, we provide a brief overview of applying machine learning
techniques for clinical prediction tasks. We begin with a quick introduction to
the concepts of machine learning and outline some of the most common machine
learning algorithms. Next, we demonstrate how to apply the algorithms with
appropriate toolkits to conduct machine learning experiments for clinical
prediction tasks. The objectives of this chapter are to (1) understand the
basics of machine learning techniques and the reasons behind why they are
useful for solving clinical prediction problems, (2) understand the intuition
behind some machine learning models, including regression, decision trees, and
support vector machines, and (3) understand how to apply these models to
clinical prediction problems using publicly available datasets via case
studies. | Disliked | zrz@andrew.cmu.edu | Machine Learning for Clinical Predictive Analytics : In this chapter, we provide a brief overview of applying machine learning
techniques for clinical prediction tasks. We begin with a quick introduction to
the concepts of machine learning and outline some of the most common machine
learning algorithms. Next, we demonstrate how to apply the algorithms with
appropriate toolkits to conduct machine learning experiments for clinical
prediction tasks. The objectives of this chapter are to (1) understand the
basics of machine learning techniques and the reasons behind why they are
useful for solving clinical prediction problems, (2) understand the intuition
behind some machine learning models, including regression, decision trees, and
support vector machines, and (3) understand how to apply these models to
clinical prediction problems using publicly available datasets via case
studies. | 0 | zrz@andrew.cmu.edu [SEP] Machine Learning for Clinical Predictive Analytics : In this chapter, we provide a brief overview of applying machine learning
techniques for clinical prediction tasks. We begin with a quick introduction to
the concepts of machine learning and outline some of the most common machine
learning algorithms. Next, we demonstrate how to apply the algorithms with
appropriate toolkits to conduct machine learning experiments for clinical
prediction tasks. The objectives of this chapter are to (1) understand the
basics of machine learning techniques and the reasons behind why they are
useful for solving clinical prediction problems, (2) understand the intuition
behind some machine learning models, including regression, decision trees, and
support vector machines, and (3) understand how to apply these models to
clinical prediction problems using publicly available datasets via case
studies. | 4 |
Augmented Body Communicator: Enhancing daily body expression for people with upper limb limitations through LLM and a robotic arm | Individuals with upper limb movement limitations face challenges in
interacting with others. Although robotic arms are currently used primarily for
functional tasks, there is considerable potential to explore ways to enhance
users' body language capabilities during social interactions. This paper
introduces an Augmented Body Communicator system that integrates robotic arms
and a large language model. Through the incorporation of kinetic memory,
disabled users and their supporters can collaboratively design actions for the
robot arm. The LLM system then provides suggestions on the most suitable action
based on contextual cues during interactions. The system underwent thorough
user testing with six participants who have conditions affecting upper limb
mobility. Results indicate that the system improves users' ability to express
themselves. Based on our findings, we offer recommendations for developing
robotic arms that support disabled individuals with body language capabilities
and functional tasks. | Liked | jechoi@andrew.cmu.edu | Augmented Body Communicator: Enhancing daily body expression for people with upper limb limitations through LLM and a robotic arm : Individuals with upper limb movement limitations face challenges in
interacting with others. Although robotic arms are currently used primarily for
functional tasks, there is considerable potential to explore ways to enhance
users' body language capabilities during social interactions. This paper
introduces an Augmented Body Communicator system that integrates robotic arms
and a large language model. Through the incorporation of kinetic memory,
disabled users and their supporters can collaboratively design actions for the
robot arm. The LLM system then provides suggestions on the most suitable action
based on contextual cues during interactions. The system underwent thorough
user testing with six participants who have conditions affecting upper limb
mobility. Results indicate that the system improves users' ability to express
themselves. Based on our findings, we offer recommendations for developing
robotic arms that support disabled individuals with body language capabilities
and functional tasks. | 1 | jechoi@andrew.cmu.edu [SEP] Augmented Body Communicator: Enhancing daily body expression for people with upper limb limitations through LLM and a robotic arm : Individuals with upper limb movement limitations face challenges in
interacting with others. Although robotic arms are currently used primarily for
functional tasks, there is considerable potential to explore ways to enhance
users' body language capabilities during social interactions. This paper
introduces an Augmented Body Communicator system that integrates robotic arms
and a large language model. Through the incorporation of kinetic memory,
disabled users and their supporters can collaboratively design actions for the
robot arm. The LLM system then provides suggestions on the most suitable action
based on contextual cues during interactions. The system underwent thorough
user testing with six participants who have conditions affecting upper limb
mobility. Results indicate that the system improves users' ability to express
themselves. Based on our findings, we offer recommendations for developing
robotic arms that support disabled individuals with body language capabilities
and functional tasks. | 466 |
Mathematical Perspective of Machine Learning | We take a closer look at some theoretical challenges of Machine Learning as a
function approximation, gradient descent as the default optimization algorithm,
limitations of fixed length and width networks and a different approach to RNNs
from a mathematical perspective. | Disliked | zrz@andrew.cmu.edu | Mathematical Perspective of Machine Learning : We take a closer look at some theoretical challenges of Machine Learning as a
function approximation, gradient descent as the default optimization algorithm,
limitations of fixed length and width networks and a different approach to RNNs
from a mathematical perspective. | 0 | zrz@andrew.cmu.edu [SEP] Mathematical Perspective of Machine Learning : We take a closer look at some theoretical challenges of Machine Learning as a
function approximation, gradient descent as the default optimization algorithm,
limitations of fixed length and width networks and a different approach to RNNs
from a mathematical perspective. | 45 |
Malleable Agents for Re-Configurable Robotic Manipulators | Re-configurable robots have more utility and flexibility for many real-world
tasks. Designing a learning agent to operate such robots requires adapting to
different configurations. Here, we focus on robotic arms with multiple rigid
links connected by joints. We propose a deep reinforcement learning agent with
sequence neural networks embedded in the agent to adapt to robotic arms that
have a varying number of links. Further, with the additional tool of domain
randomization, this agent adapts to different configurations. We perform
simulations on a 2D N-link arm to show the ability of our network to transfer
and generalize efficiently. | Liked | jechoi@andrew.cmu.edu | Malleable Agents for Re-Configurable Robotic Manipulators : Re-configurable robots have more utility and flexibility for many real-world
tasks. Designing a learning agent to operate such robots requires adapting to
different configurations. Here, we focus on robotic arms with multiple rigid
links connected by joints. We propose a deep reinforcement learning agent with
sequence neural networks embedded in the agent to adapt to robotic arms that
have a varying number of links. Further, with the additional tool of domain
randomization, this agent adapts to different configurations. We perform
simulations on a 2D N-link arm to show the ability of our network to transfer
and generalize efficiently. | 1 | jechoi@andrew.cmu.edu [SEP] Malleable Agents for Re-Configurable Robotic Manipulators : Re-configurable robots have more utility and flexibility for many real-world
tasks. Designing a learning agent to operate such robots requires adapting to
different configurations. Here, we focus on robotic arms with multiple rigid
links connected by joints. We propose a deep reinforcement learning agent with
sequence neural networks embedded in the agent to adapt to robotic arms that
have a varying number of links. Further, with the additional tool of domain
randomization, this agent adapts to different configurations. We perform
simulations on a 2D N-link arm to show the ability of our network to transfer
and generalize efficiently. | 428 |
Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task | Wire-harnessing tasks pose great challenges to be automated by the robot due
to the complex dynamics and unpredictable behavior of the deformable wire.
Traditional methods, often reliant on dual-robot arms or tactile sensing, face
limitations in adaptability, cost, and scalability. This paper introduces a
novel single-robot wire-harnessing pipeline that leverages a robot's twisting
motion to generate necessary wire tension for precise insertion into clamps,
using only one robot arm with an integrated force/torque (F/T) sensor.
Benefiting from this design, the single robot arm can efficiently apply tension
for wire routing and insertion into clamps in a narrow space. Our approach is
structured around four principal components: a Model Predictive Control (MPC)
based on the Koopman operator for tension tracking and wire following, a motion
planner for sequencing harnessing waypoints, a suite of insertion primitives
for clamp engagement, and a fix-point switching mechanism for wire constraint
updating. Evaluated on an industrial-level wire harnessing task, our method
demonstrated superior performance and reliability over conventional approaches,
efficiently handling both single and multiple wire configurations with high
success rates. | Liked | jechoi@andrew.cmu.edu | Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task : Wire-harnessing tasks pose great challenges to be automated by the robot due
to the complex dynamics and unpredictable behavior of the deformable wire.
Traditional methods, often reliant on dual-robot arms or tactile sensing, face
limitations in adaptability, cost, and scalability. This paper introduces a
novel single-robot wire-harnessing pipeline that leverages a robot's twisting
motion to generate necessary wire tension for precise insertion into clamps,
using only one robot arm with an integrated force/torque (F/T) sensor.
Benefiting from this design, the single robot arm can efficiently apply tension
for wire routing and insertion into clamps in a narrow space. Our approach is
structured around four principal components: a Model Predictive Control (MPC)
based on the Koopman operator for tension tracking and wire following, a motion
planner for sequencing harnessing waypoints, a suite of insertion primitives
for clamp engagement, and a fix-point switching mechanism for wire constraint
updating. Evaluated on an industrial-level wire harnessing task, our method
demonstrated superior performance and reliability over conventional approaches,
efficiently handling both single and multiple wire configurations with high
success rates. | 1 | jechoi@andrew.cmu.edu [SEP] Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task : Wire-harnessing tasks pose great challenges to be automated by the robot due
to the complex dynamics and unpredictable behavior of the deformable wire.
Traditional methods, often reliant on dual-robot arms or tactile sensing, face
limitations in adaptability, cost, and scalability. This paper introduces a
novel single-robot wire-harnessing pipeline that leverages a robot's twisting
motion to generate necessary wire tension for precise insertion into clamps,
using only one robot arm with an integrated force/torque (F/T) sensor.
Benefiting from this design, the single robot arm can efficiently apply tension
for wire routing and insertion into clamps in a narrow space. Our approach is
structured around four principal components: a Model Predictive Control (MPC)
based on the Koopman operator for tension tracking and wire following, a motion
planner for sequencing harnessing waypoints, a suite of insertion primitives
for clamp engagement, and a fix-point switching mechanism for wire constraint
updating. Evaluated on an industrial-level wire harnessing task, our method
demonstrated superior performance and reliability over conventional approaches,
efficiently handling both single and multiple wire configurations with high
success rates. | 491 |
Metal Wire Manipulation Planning for 3D Curving -- How a Low Payload Robot Can Use a Bending Machine to Bend Stiff Metal Wire | This paper presents a combined task and motion planner for a robot arm to
carry out 3D metal wire curving tasks by collaborating with a bending machine.
We assume a collaborative robot that is safe to work in a human environment but
has a weak payload to bend objects with large stiffness, and developed a
combined planner for the robot to use a bending machine. Our method converts a
3D curve to a bending set and generates the feasible bending sequence, machine
usage, robotic grasp poses, and pick-and-place arm motion considering the
combined task and motion level constraints. Compared with previous deformable
linear object shaping work that relied on forces provided by robotic arms, the
proposed method is suitable for the material with high stiffness. We evaluate
the system using different tasks. The results show that the proposed system is
flexible and robust to generate robotic motion to corporate with the designed
bending machine. | Liked | jechoi@andrew.cmu.edu | Metal Wire Manipulation Planning for 3D Curving -- How a Low Payload Robot Can Use a Bending Machine to Bend Stiff Metal Wire : This paper presents a combined task and motion planner for a robot arm to
carry out 3D metal wire curving tasks by collaborating with a bending machine.
We assume a collaborative robot that is safe to work in a human environment but
has a weak payload to bend objects with large stiffness, and developed a
combined planner for the robot to use a bending machine. Our method converts a
3D curve to a bending set and generates the feasible bending sequence, machine
usage, robotic grasp poses, and pick-and-place arm motion considering the
combined task and motion level constraints. Compared with previous deformable
linear object shaping work that relied on forces provided by robotic arms, the
proposed method is suitable for the material with high stiffness. We evaluate
the system using different tasks. The results show that the proposed system is
flexible and robust to generate robotic motion to corporate with the designed
bending machine. | 1 | jechoi@andrew.cmu.edu [SEP] Metal Wire Manipulation Planning for 3D Curving -- How a Low Payload Robot Can Use a Bending Machine to Bend Stiff Metal Wire : This paper presents a combined task and motion planner for a robot arm to
carry out 3D metal wire curving tasks by collaborating with a bending machine.
We assume a collaborative robot that is safe to work in a human environment but
has a weak payload to bend objects with large stiffness, and developed a
combined planner for the robot to use a bending machine. Our method converts a
3D curve to a bending set and generates the feasible bending sequence, machine
usage, robotic grasp poses, and pick-and-place arm motion considering the
combined task and motion level constraints. Compared with previous deformable
linear object shaping work that relied on forces provided by robotic arms, the
proposed method is suitable for the material with high stiffness. We evaluate
the system using different tasks. The results show that the proposed system is
flexible and robust to generate robotic motion to corporate with the designed
bending machine. | 530 |
Ethics and Creativity in Computer Vision | This paper offers a retrospective of what we learnt from organizing the
workshop *Ethical Considerations in Creative applications of Computer Vision*
at CVPR 2021 conference and, prior to that, a series of workshops on *Computer
Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We
hope this reflection will bring artists and machine learning researchers into
conversation around the ethical and social dimensions of creative applications
of computer vision. | Disliked | zrz@andrew.cmu.edu | Ethics and Creativity in Computer Vision : This paper offers a retrospective of what we learnt from organizing the
workshop *Ethical Considerations in Creative applications of Computer Vision*
at CVPR 2021 conference and, prior to that, a series of workshops on *Computer
Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We
hope this reflection will bring artists and machine learning researchers into
conversation around the ethical and social dimensions of creative applications
of computer vision. | 0 | zrz@andrew.cmu.edu [SEP] Ethics and Creativity in Computer Vision : This paper offers a retrospective of what we learnt from organizing the
workshop *Ethical Considerations in Creative applications of Computer Vision*
at CVPR 2021 conference and, prior to that, a series of workshops on *Computer
Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We
hope this reflection will bring artists and machine learning researchers into
conversation around the ethical and social dimensions of creative applications
of computer vision. | 361 |
Transferability in Deep Learning: A Survey | The success of deep learning algorithms generally depends on large-scale
data, while humans appear to have inherent ability of knowledge transfer, by
recognizing and applying relevant knowledge from previous learning experiences
when encountering and solving unseen tasks. Such an ability to acquire and
reuse knowledge is known as transferability in deep learning. It has formed the
long-term quest towards making deep learning as data-efficient as human
learning, and has been motivating fruitful design of more powerful deep
learning algorithms. We present this survey to connect different isolated areas
in deep learning with their relation to transferability, and to provide a
unified and complete view to investigating transferability through the whole
lifecycle of deep learning. The survey elaborates the fundamental goals and
challenges in parallel with the core principles and methods, covering recent
cornerstones in deep architectures, pre-training, task adaptation and domain
adaptation. This highlights unanswered questions on the appropriate objectives
for learning transferable knowledge and for adapting the knowledge to new tasks
and domains, avoiding catastrophic forgetting and negative transfer. Finally,
we implement a benchmark and an open-source library, enabling a fair evaluation
of deep learning methods in terms of transferability. | Disliked | zrz@andrew.cmu.edu | Transferability in Deep Learning: A Survey : The success of deep learning algorithms generally depends on large-scale
data, while humans appear to have inherent ability of knowledge transfer, by
recognizing and applying relevant knowledge from previous learning experiences
when encountering and solving unseen tasks. Such an ability to acquire and
reuse knowledge is known as transferability in deep learning. It has formed the
long-term quest towards making deep learning as data-efficient as human
learning, and has been motivating fruitful design of more powerful deep
learning algorithms. We present this survey to connect different isolated areas
in deep learning with their relation to transferability, and to provide a
unified and complete view to investigating transferability through the whole
lifecycle of deep learning. The survey elaborates the fundamental goals and
challenges in parallel with the core principles and methods, covering recent
cornerstones in deep architectures, pre-training, task adaptation and domain
adaptation. This highlights unanswered questions on the appropriate objectives
for learning transferable knowledge and for adapting the knowledge to new tasks
and domains, avoiding catastrophic forgetting and negative transfer. Finally,
we implement a benchmark and an open-source library, enabling a fair evaluation
of deep learning methods in terms of transferability. | 0 | zrz@andrew.cmu.edu [SEP] Transferability in Deep Learning: A Survey : The success of deep learning algorithms generally depends on large-scale
data, while humans appear to have inherent ability of knowledge transfer, by
recognizing and applying relevant knowledge from previous learning experiences
when encountering and solving unseen tasks. Such an ability to acquire and
reuse knowledge is known as transferability in deep learning. It has formed the
long-term quest towards making deep learning as data-efficient as human
learning, and has been motivating fruitful design of more powerful deep
learning algorithms. We present this survey to connect different isolated areas
in deep learning with their relation to transferability, and to provide a
unified and complete view to investigating transferability through the whole
lifecycle of deep learning. The survey elaborates the fundamental goals and
challenges in parallel with the core principles and methods, covering recent
cornerstones in deep architectures, pre-training, task adaptation and domain
adaptation. This highlights unanswered questions on the appropriate objectives
for learning transferable knowledge and for adapting the knowledge to new tasks
and domains, avoiding catastrophic forgetting and negative transfer. Finally,
we implement a benchmark and an open-source library, enabling a fair evaluation
of deep learning methods in terms of transferability. | 177 |
Learning Dexterous Manipulation with Quantized Hand State | Dexterous robotic hands enable robots to perform complex manipulations that
require fine-grained control and adaptability. Achieving such manipulation is
challenging because the high degrees of freedom tightly couple hand and arm
motions, making learning and control difficult. Successful dexterous
manipulation relies not only on precise hand motions, but also on accurate
spatial positioning of the arm and coordinated arm-hand dynamics. However, most
existing visuomotor policies represent arm and hand actions in a single
combined space, which often causes high-dimensional hand actions to dominate
the coupled action space and compromise arm control. To address this, we
propose DQ-RISE, which quantizes hand states to simplify hand motion prediction
while preserving essential patterns, and applies a continuous relaxation that
allows arm actions to diffuse jointly with these compact hand states. This
design enables the policy to learn arm-hand coordination from data while
preventing hand actions from overwhelming the action space. Experiments show
that DQ-RISE achieves more balanced and efficient learning, paving the way
toward structured and generalizable dexterous manipulation. Project website:
http://rise-policy.github.io/DQ-RISE/ | Liked | jechoi@andrew.cmu.edu | Learning Dexterous Manipulation with Quantized Hand State : Dexterous robotic hands enable robots to perform complex manipulations that
require fine-grained control and adaptability. Achieving such manipulation is
challenging because the high degrees of freedom tightly couple hand and arm
motions, making learning and control difficult. Successful dexterous
manipulation relies not only on precise hand motions, but also on accurate
spatial positioning of the arm and coordinated arm-hand dynamics. However, most
existing visuomotor policies represent arm and hand actions in a single
combined space, which often causes high-dimensional hand actions to dominate
the coupled action space and compromise arm control. To address this, we
propose DQ-RISE, which quantizes hand states to simplify hand motion prediction
while preserving essential patterns, and applies a continuous relaxation that
allows arm actions to diffuse jointly with these compact hand states. This
design enables the policy to learn arm-hand coordination from data while
preventing hand actions from overwhelming the action space. Experiments show
that DQ-RISE achieves more balanced and efficient learning, paving the way
toward structured and generalizable dexterous manipulation. Project website:
http://rise-policy.github.io/DQ-RISE/ | 1 | jechoi@andrew.cmu.edu [SEP] Learning Dexterous Manipulation with Quantized Hand State : Dexterous robotic hands enable robots to perform complex manipulations that
require fine-grained control and adaptability. Achieving such manipulation is
challenging because the high degrees of freedom tightly couple hand and arm
motions, making learning and control difficult. Successful dexterous
manipulation relies not only on precise hand motions, but also on accurate
spatial positioning of the arm and coordinated arm-hand dynamics. However, most
existing visuomotor policies represent arm and hand actions in a single
combined space, which often causes high-dimensional hand actions to dominate
the coupled action space and compromise arm control. To address this, we
propose DQ-RISE, which quantizes hand states to simplify hand motion prediction
while preserving essential patterns, and applies a continuous relaxation that
allows arm actions to diffuse jointly with these compact hand states. This
design enables the policy to learn arm-hand coordination from data while
preventing hand actions from overwhelming the action space. Experiments show
that DQ-RISE achieves more balanced and efficient learning, paving the way
toward structured and generalizable dexterous manipulation. Project website:
http://rise-policy.github.io/DQ-RISE/ | 471 |
Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches | This is an extended abstract for a presentation at The 17th International
Conference on CUPUM - Computational Urban Planning and Urban Management in June
2021. This study presents an interdisciplinary synthesis of the
state-of-the-art approaches in computer vision technologies to extract built
environment features that could improve the robustness of empirical research in
planning. We discussed the findings from the review of studies in both planning
and computer science. | Disliked | zrz@andrew.cmu.edu | Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches : This is an extended abstract for a presentation at The 17th International
Conference on CUPUM - Computational Urban Planning and Urban Management in June
2021. This study presents an interdisciplinary synthesis of the
state-of-the-art approaches in computer vision technologies to extract built
environment features that could improve the robustness of empirical research in
planning. We discussed the findings from the review of studies in both planning
and computer science. | 0 | zrz@andrew.cmu.edu [SEP] Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches : This is an extended abstract for a presentation at The 17th International
Conference on CUPUM - Computational Urban Planning and Urban Management in June
2021. This study presents an interdisciplinary synthesis of the
state-of-the-art approaches in computer vision technologies to extract built
environment features that could improve the robustness of empirical research in
planning. We discussed the findings from the review of studies in both planning
and computer science. | 352 |
Lale: Consistent Automated Machine Learning | Automated machine learning makes it easier for data scientists to develop
pipelines by searching over possible choices for hyperparameters, algorithms,
and even pipeline topologies. Unfortunately, the syntax for automated machine
learning tools is inconsistent with manual machine learning, with each other,
and with error checks. Furthermore, few tools support advanced features such as
topology search or higher-order operators. This paper introduces Lale, a
library of high-level Python interfaces that simplifies and unifies automated
machine learning in a consistent way. | Liked | zrz@andrew.cmu.edu | Lale: Consistent Automated Machine Learning : Automated machine learning makes it easier for data scientists to develop
pipelines by searching over possible choices for hyperparameters, algorithms,
and even pipeline topologies. Unfortunately, the syntax for automated machine
learning tools is inconsistent with manual machine learning, with each other,
and with error checks. Furthermore, few tools support advanced features such as
topology search or higher-order operators. This paper introduces Lale, a
library of high-level Python interfaces that simplifies and unifies automated
machine learning in a consistent way. | 1 | zrz@andrew.cmu.edu [SEP] Lale: Consistent Automated Machine Learning : Automated machine learning makes it easier for data scientists to develop
pipelines by searching over possible choices for hyperparameters, algorithms,
and even pipeline topologies. Unfortunately, the syntax for automated machine
learning tools is inconsistent with manual machine learning, with each other,
and with error checks. Furthermore, few tools support advanced features such as
topology search or higher-order operators. This paper introduces Lale, a
library of high-level Python interfaces that simplifies and unifies automated
machine learning in a consistent way. | 74 |
Faster Deep Q-learning using Neural Episodic Control | The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. We propose NEC2DQN that
improves learning speed of a poor sample efficiency algorithm such as DQN by
using good one such as NEC at the beginning of learning. We show it is able to
learn faster than Double DQN or N-step DQN in the experiments of Pong. | Disliked | zrz@andrew.cmu.edu | Faster Deep Q-learning using Neural Episodic Control : The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. We propose NEC2DQN that
improves learning speed of a poor sample efficiency algorithm such as DQN by
using good one such as NEC at the beginning of learning. We show it is able to
learn faster than Double DQN or N-step DQN in the experiments of Pong. | 0 | zrz@andrew.cmu.edu [SEP] Faster Deep Q-learning using Neural Episodic Control : The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. We propose NEC2DQN that
improves learning speed of a poor sample efficiency algorithm such as DQN by
using good one such as NEC at the beginning of learning. We show it is able to
learn faster than Double DQN or N-step DQN in the experiments of Pong. | 253 |
Machine Learning as Ecology | Machine learning methods have had spectacular success on numerous problems.
Here we show that a prominent class of learning algorithms - including Support
Vector Machines (SVMs) -- have a natural interpretation in terms of ecological
dynamics. We use these ideas to design new online SVM algorithms that exploit
ecological invasions, and benchmark performance using the MNIST dataset. Our
work provides a new ecological lens through which we can view statistical
learning and opens the possibility of designing ecosystems for machine
learning.
Supplemental code is found at https://github.com/owenhowell20/EcoSVM. | Disliked | zrz@andrew.cmu.edu | Machine Learning as Ecology : Machine learning methods have had spectacular success on numerous problems.
Here we show that a prominent class of learning algorithms - including Support
Vector Machines (SVMs) -- have a natural interpretation in terms of ecological
dynamics. We use these ideas to design new online SVM algorithms that exploit
ecological invasions, and benchmark performance using the MNIST dataset. Our
work provides a new ecological lens through which we can view statistical
learning and opens the possibility of designing ecosystems for machine
learning.
Supplemental code is found at https://github.com/owenhowell20/EcoSVM. | 0 | zrz@andrew.cmu.edu [SEP] Machine Learning as Ecology : Machine learning methods have had spectacular success on numerous problems.
Here we show that a prominent class of learning algorithms - including Support
Vector Machines (SVMs) -- have a natural interpretation in terms of ecological
dynamics. We use these ideas to design new online SVM algorithms that exploit
ecological invasions, and benchmark performance using the MNIST dataset. Our
work provides a new ecological lens through which we can view statistical
learning and opens the possibility of designing ecosystems for machine
learning.
Supplemental code is found at https://github.com/owenhowell20/EcoSVM. | 106 |
Data Pricing in Machine Learning Pipelines | Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as
pipelines in an eco-system, such as collecting data for possible machine
learning applications, collaboratively training models by multiple parties and
delivering machine learning services to end users. Data is critical and
penetrating in the whole machine learning pipelines. As machine learning
pipelines involve many parties and, in order to be successful, have to form a
constructive and dynamic eco-system, marketplaces and data pricing are
fundamental in connecting and facilitating those many parties. In this article,
we survey the principles and the latest research development of data pricing in
machine learning pipelines. We start with a brief review of data marketplaces
and pricing desiderata. Then, we focus on pricing in three important steps in
machine learning pipelines. To understand pricing in the step of training data
collection, we review pricing raw data sets and data labels. We also
investigate pricing in the step of collaborative training of machine learning
models, and overview pricing machine learning models for end users in the step
of machine learning deployment. We also discuss a series of possible future
directions. | Liked | zrz@andrew.cmu.edu | Data Pricing in Machine Learning Pipelines : Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as
pipelines in an eco-system, such as collecting data for possible machine
learning applications, collaboratively training models by multiple parties and
delivering machine learning services to end users. Data is critical and
penetrating in the whole machine learning pipelines. As machine learning
pipelines involve many parties and, in order to be successful, have to form a
constructive and dynamic eco-system, marketplaces and data pricing are
fundamental in connecting and facilitating those many parties. In this article,
we survey the principles and the latest research development of data pricing in
machine learning pipelines. We start with a brief review of data marketplaces
and pricing desiderata. Then, we focus on pricing in three important steps in
machine learning pipelines. To understand pricing in the step of training data
collection, we review pricing raw data sets and data labels. We also
investigate pricing in the step of collaborative training of machine learning
models, and overview pricing machine learning models for end users in the step
of machine learning deployment. We also discuss a series of possible future
directions. | 1 | zrz@andrew.cmu.edu [SEP] Data Pricing in Machine Learning Pipelines : Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as
pipelines in an eco-system, such as collecting data for possible machine
learning applications, collaboratively training models by multiple parties and
delivering machine learning services to end users. Data is critical and
penetrating in the whole machine learning pipelines. As machine learning
pipelines involve many parties and, in order to be successful, have to form a
constructive and dynamic eco-system, marketplaces and data pricing are
fundamental in connecting and facilitating those many parties. In this article,
we survey the principles and the latest research development of data pricing in
machine learning pipelines. We start with a brief review of data marketplaces
and pricing desiderata. Then, we focus on pricing in three important steps in
machine learning pipelines. To understand pricing in the step of training data
collection, we review pricing raw data sets and data labels. We also
investigate pricing in the step of collaborative training of machine learning
models, and overview pricing machine learning models for end users in the step
of machine learning deployment. We also discuss a series of possible future
directions. | 36 |
Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization | Interest in soft continuum arms has increased as their inherent material
elasticity enables safe and adaptive interactions with the environment. However
to achieve full autonomy in these arms, accurate three-dimensional shape
sensing is needed. Vision-based solutions have been found to be effective in
estimating the shape of soft continuum arms. In this paper, a vision-based
shape estimator that utilizes a geometric strain based representation for the
soft continuum arm's shape, is proposed. This representation reduces the
dimension of the curved shape to a finite set of strain basis functions,
thereby allowing for efficient optimization for the shape that best fits the
observed image. Experimental results demonstrate the effectiveness of the
proposed approach in estimating the end effector with accuracy less than the
soft arm's radius. Multiple basis functions are also analyzed and compared for
the specific soft continuum arm in use. | Liked | jechoi@andrew.cmu.edu | Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization : Interest in soft continuum arms has increased as their inherent material
elasticity enables safe and adaptive interactions with the environment. However
to achieve full autonomy in these arms, accurate three-dimensional shape
sensing is needed. Vision-based solutions have been found to be effective in
estimating the shape of soft continuum arms. In this paper, a vision-based
shape estimator that utilizes a geometric strain based representation for the
soft continuum arm's shape, is proposed. This representation reduces the
dimension of the curved shape to a finite set of strain basis functions,
thereby allowing for efficient optimization for the shape that best fits the
observed image. Experimental results demonstrate the effectiveness of the
proposed approach in estimating the end effector with accuracy less than the
soft arm's radius. Multiple basis functions are also analyzed and compared for
the specific soft continuum arm in use. | 1 | jechoi@andrew.cmu.edu [SEP] Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization : Interest in soft continuum arms has increased as their inherent material
elasticity enables safe and adaptive interactions with the environment. However
to achieve full autonomy in these arms, accurate three-dimensional shape
sensing is needed. Vision-based solutions have been found to be effective in
estimating the shape of soft continuum arms. In this paper, a vision-based
shape estimator that utilizes a geometric strain based representation for the
soft continuum arm's shape, is proposed. This representation reduces the
dimension of the curved shape to a finite set of strain basis functions,
thereby allowing for efficient optimization for the shape that best fits the
observed image. Experimental results demonstrate the effectiveness of the
proposed approach in estimating the end effector with accuracy less than the
soft arm's radius. Multiple basis functions are also analyzed and compared for
the specific soft continuum arm in use. | 558 |
Information Theory and its Relation to Machine Learning | In this position paper, I first describe a new perspective on machine
learning (ML) by four basic problems (or levels), namely, "What to learn?",
"How to learn?", "What to evaluate?", and "What to adjust?". The paper stresses
more on the first level of "What to learn?", or "Learning Target Selection".
Towards this primary problem within the four levels, I briefly review the
existing studies about the connection between information theoretical learning
(ITL [1]) and machine learning. A theorem is given on the relation between the
empirically-defined similarity measure and information measures. Finally, a
conjecture is proposed for pursuing a unified mathematical interpretation to
learning target selection. | Disliked | zrz@andrew.cmu.edu | Information Theory and its Relation to Machine Learning : In this position paper, I first describe a new perspective on machine
learning (ML) by four basic problems (or levels), namely, "What to learn?",
"How to learn?", "What to evaluate?", and "What to adjust?". The paper stresses
more on the first level of "What to learn?", or "Learning Target Selection".
Towards this primary problem within the four levels, I briefly review the
existing studies about the connection between information theoretical learning
(ITL [1]) and machine learning. A theorem is given on the relation between the
empirically-defined similarity measure and information measures. Finally, a
conjecture is proposed for pursuing a unified mathematical interpretation to
learning target selection. | 0 | zrz@andrew.cmu.edu [SEP] Information Theory and its Relation to Machine Learning : In this position paper, I first describe a new perspective on machine
learning (ML) by four basic problems (or levels), namely, "What to learn?",
"How to learn?", "What to evaluate?", and "What to adjust?". The paper stresses
more on the first level of "What to learn?", or "Learning Target Selection".
Towards this primary problem within the four levels, I briefly review the
existing studies about the connection between information theoretical learning
(ITL [1]) and machine learning. A theorem is given on the relation between the
empirically-defined similarity measure and information measures. Finally, a
conjecture is proposed for pursuing a unified mathematical interpretation to
learning target selection. | 124 |
A New Robot Arm Calibration Method Based on Cubic Interpolated Beetle Antennae Search Approach | Industrial robot arms are extensively important for intelligent
manufacturing. An industrial robot arm commonly enjoys its high repetitive
positioning accuracy while suffering from its low absolute positioning
accuracy, which greatly restricts its application in high-precision
manufacture, like automobile manufacture. Aiming at addressing this hot issue,
this work proposes a novel robot arm calibration method based on cubic
interpolated beetle antennae search (CIBAS). This study has three ideas: a)
developing a novel CIBAS algorithm, which can effectively addresses the issue
of local optimum in a Beetle Antennae Search algorithm; b) utilizing a particle
filter to reduce the influence of non-Gaussian noises; and c) proposing a new
calibration method incorporating CIBAS algorithm and particle filter to
searching the optimal kinematic parameters. Experimental results on an ABB
IRB120 industrial robot arm demonstrate that the proposed method achieves much
higher calibration accuracy than several state-of-the-art calibration methods. | Liked | jechoi@andrew.cmu.edu | A New Robot Arm Calibration Method Based on Cubic Interpolated Beetle Antennae Search Approach : Industrial robot arms are extensively important for intelligent
manufacturing. An industrial robot arm commonly enjoys its high repetitive
positioning accuracy while suffering from its low absolute positioning
accuracy, which greatly restricts its application in high-precision
manufacture, like automobile manufacture. Aiming at addressing this hot issue,
this work proposes a novel robot arm calibration method based on cubic
interpolated beetle antennae search (CIBAS). This study has three ideas: a)
developing a novel CIBAS algorithm, which can effectively addresses the issue
of local optimum in a Beetle Antennae Search algorithm; b) utilizing a particle
filter to reduce the influence of non-Gaussian noises; and c) proposing a new
calibration method incorporating CIBAS algorithm and particle filter to
searching the optimal kinematic parameters. Experimental results on an ABB
IRB120 industrial robot arm demonstrate that the proposed method achieves much
higher calibration accuracy than several state-of-the-art calibration methods. | 1 | jechoi@andrew.cmu.edu [SEP] A New Robot Arm Calibration Method Based on Cubic Interpolated Beetle Antennae Search Approach : Industrial robot arms are extensively important for intelligent
manufacturing. An industrial robot arm commonly enjoys its high repetitive
positioning accuracy while suffering from its low absolute positioning
accuracy, which greatly restricts its application in high-precision
manufacture, like automobile manufacture. Aiming at addressing this hot issue,
this work proposes a novel robot arm calibration method based on cubic
interpolated beetle antennae search (CIBAS). This study has three ideas: a)
developing a novel CIBAS algorithm, which can effectively addresses the issue
of local optimum in a Beetle Antennae Search algorithm; b) utilizing a particle
filter to reduce the influence of non-Gaussian noises; and c) proposing a new
calibration method incorporating CIBAS algorithm and particle filter to
searching the optimal kinematic parameters. Experimental results on an ABB
IRB120 industrial robot arm demonstrate that the proposed method achieves much
higher calibration accuracy than several state-of-the-art calibration methods. | 463 |
Ten-year Survival Prediction for Breast Cancer Patients | This report assesses different machine learning approaches to 10-year
survival prediction of breast cancer patients. | Disliked | zrz@andrew.cmu.edu | Ten-year Survival Prediction for Breast Cancer Patients : This report assesses different machine learning approaches to 10-year
survival prediction of breast cancer patients. | 0 | zrz@andrew.cmu.edu [SEP] Ten-year Survival Prediction for Breast Cancer Patients : This report assesses different machine learning approaches to 10-year
survival prediction of breast cancer patients. | 47 |
Discussion on Mechanical Learning and Learning Machine | Mechanical learning is a computing system that is based on a set of simple
and fixed rules, and can learn from incoming data. A learning machine is a
system that realizes mechanical learning. Importantly, we emphasis that it is
based on a set of simple and fixed rules, contrasting to often called machine
learning that is sophisticated software based on very complicated mathematical
theory, and often needs human intervene for software fine tune and manual
adjustments. Here, we discuss some basic facts and principles of such system,
and try to lay down a framework for further study. We propose 2 directions to
approach mechanical learning, just like Church-Turing pair: one is trying to
realize a learning machine, another is trying to well describe the mechanical
learning. | Disliked | zrz@andrew.cmu.edu | Discussion on Mechanical Learning and Learning Machine : Mechanical learning is a computing system that is based on a set of simple
and fixed rules, and can learn from incoming data. A learning machine is a
system that realizes mechanical learning. Importantly, we emphasis that it is
based on a set of simple and fixed rules, contrasting to often called machine
learning that is sophisticated software based on very complicated mathematical
theory, and often needs human intervene for software fine tune and manual
adjustments. Here, we discuss some basic facts and principles of such system,
and try to lay down a framework for further study. We propose 2 directions to
approach mechanical learning, just like Church-Turing pair: one is trying to
realize a learning machine, another is trying to well describe the mechanical
learning. | 0 | zrz@andrew.cmu.edu [SEP] Discussion on Mechanical Learning and Learning Machine : Mechanical learning is a computing system that is based on a set of simple
and fixed rules, and can learn from incoming data. A learning machine is a
system that realizes mechanical learning. Importantly, we emphasis that it is
based on a set of simple and fixed rules, contrasting to often called machine
learning that is sophisticated software based on very complicated mathematical
theory, and often needs human intervene for software fine tune and manual
adjustments. Here, we discuss some basic facts and principles of such system,
and try to lay down a framework for further study. We propose 2 directions to
approach mechanical learning, just like Church-Turing pair: one is trying to
realize a learning machine, another is trying to well describe the mechanical
learning. | 125 |
Research Experience of an Undergraduate Student in Computer Vision and Robotics | This paper focuses on the educational journey of a computer engineering
undergraduate student venturing into the domain of computer vision and
robotics. It explores how optical flow and its applications can be used to
detect moving objects when a camera undergoes translational motion,
highlighting the challenges encountered and the strategies used to overcome
them. Furthermore, the paper discusses not only the technical skills acquired
by the student but also interpersonal skills as related to teamwork and
diversity. In this paper, we detail the learning process, including the
acquisition of technical and problem-solving skills, as well as out-of-the-box
thinking. | Disliked | zrz@andrew.cmu.edu | Research Experience of an Undergraduate Student in Computer Vision and Robotics : This paper focuses on the educational journey of a computer engineering
undergraduate student venturing into the domain of computer vision and
robotics. It explores how optical flow and its applications can be used to
detect moving objects when a camera undergoes translational motion,
highlighting the challenges encountered and the strategies used to overcome
them. Furthermore, the paper discusses not only the technical skills acquired
by the student but also interpersonal skills as related to teamwork and
diversity. In this paper, we detail the learning process, including the
acquisition of technical and problem-solving skills, as well as out-of-the-box
thinking. | 0 | zrz@andrew.cmu.edu [SEP] Research Experience of an Undergraduate Student in Computer Vision and Robotics : This paper focuses on the educational journey of a computer engineering
undergraduate student venturing into the domain of computer vision and
robotics. It explores how optical flow and its applications can be used to
detect moving objects when a camera undergoes translational motion,
highlighting the challenges encountered and the strategies used to overcome
them. Furthermore, the paper discusses not only the technical skills acquired
by the student but also interpersonal skills as related to teamwork and
diversity. In this paper, we detail the learning process, including the
acquisition of technical and problem-solving skills, as well as out-of-the-box
thinking. | 381 |
Prioritized Hierarchical Compliance Control for Dual-Arm Robot Stable Clamping | When a dual-arm robot clamps a rigid object in an environment for human
beings, the environment or the collaborating human will impose incidental
disturbance on the operated object or the robot arm, leading to clamping
failure, damaging the robot even hurting the human. This research proposes a
prioritized hierarchical compliance control to simultaneously deal with the two
types of disturbances in the dual-arm robot clamping. First, we use
hierarchical quadratic programming (HQP) to solve the robot inverse kinematics
under the joint constraints and prioritize the compliance for the disturbance
on the object over that on the robot arm. Second, we estimate the disturbance
forces throughout the momentum observer with the F/T sensors and adopt
admittance control to realize the compliances. Finally, we perform the verify
experiments on a 14-DOF position-controlled dual-arm robot WalkerX, clamping a
rigid object stably while realizing the compliance against the disturbances. | Liked | jechoi@andrew.cmu.edu | Prioritized Hierarchical Compliance Control for Dual-Arm Robot Stable Clamping : When a dual-arm robot clamps a rigid object in an environment for human
beings, the environment or the collaborating human will impose incidental
disturbance on the operated object or the robot arm, leading to clamping
failure, damaging the robot even hurting the human. This research proposes a
prioritized hierarchical compliance control to simultaneously deal with the two
types of disturbances in the dual-arm robot clamping. First, we use
hierarchical quadratic programming (HQP) to solve the robot inverse kinematics
under the joint constraints and prioritize the compliance for the disturbance
on the object over that on the robot arm. Second, we estimate the disturbance
forces throughout the momentum observer with the F/T sensors and adopt
admittance control to realize the compliances. Finally, we perform the verify
experiments on a 14-DOF position-controlled dual-arm robot WalkerX, clamping a
rigid object stably while realizing the compliance against the disturbances. | 1 | jechoi@andrew.cmu.edu [SEP] Prioritized Hierarchical Compliance Control for Dual-Arm Robot Stable Clamping : When a dual-arm robot clamps a rigid object in an environment for human
beings, the environment or the collaborating human will impose incidental
disturbance on the operated object or the robot arm, leading to clamping
failure, damaging the robot even hurting the human. This research proposes a
prioritized hierarchical compliance control to simultaneously deal with the two
types of disturbances in the dual-arm robot clamping. First, we use
hierarchical quadratic programming (HQP) to solve the robot inverse kinematics
under the joint constraints and prioritize the compliance for the disturbance
on the object over that on the robot arm. Second, we estimate the disturbance
forces throughout the momentum observer with the F/T sensors and adopt
admittance control to realize the compliances. Finally, we perform the verify
experiments on a 14-DOF position-controlled dual-arm robot WalkerX, clamping a
rigid object stably while realizing the compliance against the disturbances. | 405 |
Examining the legibility of humanoid robot arm movements in a pointing task | Human--robot interaction requires robots whose actions are legible, allowing
humans to interpret, predict, and feel safe around them. This study
investigates the legibility of humanoid robot arm movements in a pointing task,
aiming to understand how humans predict robot intentions from truncated
movements and bodily cues. We designed an experiment using the NICO humanoid
robot, where participants observed its arm movements towards targets on a
touchscreen. Robot cues varied across conditions: gaze, pointing, and pointing
with congruent or incongruent gaze. Arm trajectories were stopped at 60\% or
80\% of their full length, and participants predicted the final target. We
tested the multimodal superiority and ocular primacy hypotheses, both of which
were supported by the experiment. | Liked | jechoi@andrew.cmu.edu | Examining the legibility of humanoid robot arm movements in a pointing task : Human--robot interaction requires robots whose actions are legible, allowing
humans to interpret, predict, and feel safe around them. This study
investigates the legibility of humanoid robot arm movements in a pointing task,
aiming to understand how humans predict robot intentions from truncated
movements and bodily cues. We designed an experiment using the NICO humanoid
robot, where participants observed its arm movements towards targets on a
touchscreen. Robot cues varied across conditions: gaze, pointing, and pointing
with congruent or incongruent gaze. Arm trajectories were stopped at 60\% or
80\% of their full length, and participants predicted the final target. We
tested the multimodal superiority and ocular primacy hypotheses, both of which
were supported by the experiment. | 1 | jechoi@andrew.cmu.edu [SEP] Examining the legibility of humanoid robot arm movements in a pointing task : Human--robot interaction requires robots whose actions are legible, allowing
humans to interpret, predict, and feel safe around them. This study
investigates the legibility of humanoid robot arm movements in a pointing task,
aiming to understand how humans predict robot intentions from truncated
movements and bodily cues. We designed an experiment using the NICO humanoid
robot, where participants observed its arm movements towards targets on a
touchscreen. Robot cues varied across conditions: gaze, pointing, and pointing
with congruent or incongruent gaze. Arm trajectories were stopped at 60\% or
80\% of their full length, and participants predicted the final target. We
tested the multimodal superiority and ocular primacy hypotheses, both of which
were supported by the experiment. | 468 |
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms | Robotic arms are widely used in automatic industries. However, with wide
applications of deep learning in robotic arms, there are new challenges such as
the allocation of grasping computing power and the growing demand for security.
In this work, we propose a robotic arm grasping approach based on deep learning
and edge-cloud collaboration. This approach realizes the arbitrary grasp
planning of the robot arm and considers the grasp efficiency and information
security. In addition, the encoder and decoder trained by GAN enable the images
to be encrypted while compressing, which ensures the security of privacy. The
model achieves 92% accuracy on the OCID dataset, the image compression ratio
reaches 0.03%, and the structural difference value is higher than 0.91. | Liked | jechoi@andrew.cmu.edu | A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms : Robotic arms are widely used in automatic industries. However, with wide
applications of deep learning in robotic arms, there are new challenges such as
the allocation of grasping computing power and the growing demand for security.
In this work, we propose a robotic arm grasping approach based on deep learning
and edge-cloud collaboration. This approach realizes the arbitrary grasp
planning of the robot arm and considers the grasp efficiency and information
security. In addition, the encoder and decoder trained by GAN enable the images
to be encrypted while compressing, which ensures the security of privacy. The
model achieves 92% accuracy on the OCID dataset, the image compression ratio
reaches 0.03%, and the structural difference value is higher than 0.91. | 1 | jechoi@andrew.cmu.edu [SEP] A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms : Robotic arms are widely used in automatic industries. However, with wide
applications of deep learning in robotic arms, there are new challenges such as
the allocation of grasping computing power and the growing demand for security.
In this work, we propose a robotic arm grasping approach based on deep learning
and edge-cloud collaboration. This approach realizes the arbitrary grasp
planning of the robot arm and considers the grasp efficiency and information
security. In addition, the encoder and decoder trained by GAN enable the images
to be encrypted while compressing, which ensures the security of privacy. The
model achieves 92% accuracy on the OCID dataset, the image compression ratio
reaches 0.03%, and the structural difference value is higher than 0.91. | 465 |
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism | Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it. | Disliked | zrz@andrew.cmu.edu | The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism : Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it. | 0 | zrz@andrew.cmu.edu [SEP] The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism : Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it. | 384 |
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models | Medical vision-language alignment through cross-modal contrastive learning
shows promising performance in image-text matching tasks, such as retrieval and
zero-shot classification. However, conventional cross-modal contrastive
learning (CLIP-based) methods suffer from suboptimal visual representation
capabilities, which also limits their effectiveness in vision-language
alignment. In contrast, although the models pretrained via multimodal masked
modeling struggle with direct cross-modal matching, they excel in visual
representation. To address this contradiction, we propose ALTA (ALign Through
Adapting), an efficient medical vision-language alignment method that utilizes
only about 8% of the trainable parameters and less than 1/5 of the
computational consumption required for masked record modeling. ALTA achieves
superior performance in vision-language matching tasks like retrieval and
zero-shot classification by adapting the pretrained vision model from masked
record modeling. Additionally, we integrate temporal-multiview radiograph
inputs to enhance the information consistency between radiographs and their
corresponding descriptions in reports, further improving the vision-language
alignment. Experimental evaluations show that ALTA outperforms the
best-performing counterpart by over 4% absolute points in text-to-image
accuracy and approximately 6% absolute points in image-to-text retrieval
accuracy. The adaptation of vision-language models during efficient alignment
also promotes better vision and language understanding. Code is publicly
available at https://github.com/DopamineLcy/ALTA. | Liked | zrz@andrew.cmu.edu | Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models : Medical vision-language alignment through cross-modal contrastive learning
shows promising performance in image-text matching tasks, such as retrieval and
zero-shot classification. However, conventional cross-modal contrastive
learning (CLIP-based) methods suffer from suboptimal visual representation
capabilities, which also limits their effectiveness in vision-language
alignment. In contrast, although the models pretrained via multimodal masked
modeling struggle with direct cross-modal matching, they excel in visual
representation. To address this contradiction, we propose ALTA (ALign Through
Adapting), an efficient medical vision-language alignment method that utilizes
only about 8% of the trainable parameters and less than 1/5 of the
computational consumption required for masked record modeling. ALTA achieves
superior performance in vision-language matching tasks like retrieval and
zero-shot classification by adapting the pretrained vision model from masked
record modeling. Additionally, we integrate temporal-multiview radiograph
inputs to enhance the information consistency between radiographs and their
corresponding descriptions in reports, further improving the vision-language
alignment. Experimental evaluations show that ALTA outperforms the
best-performing counterpart by over 4% absolute points in text-to-image
accuracy and approximately 6% absolute points in image-to-text retrieval
accuracy. The adaptation of vision-language models during efficient alignment
also promotes better vision and language understanding. Code is publicly
available at https://github.com/DopamineLcy/ALTA. | 1 | zrz@andrew.cmu.edu [SEP] Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models : Medical vision-language alignment through cross-modal contrastive learning
shows promising performance in image-text matching tasks, such as retrieval and
zero-shot classification. However, conventional cross-modal contrastive
learning (CLIP-based) methods suffer from suboptimal visual representation
capabilities, which also limits their effectiveness in vision-language
alignment. In contrast, although the models pretrained via multimodal masked
modeling struggle with direct cross-modal matching, they excel in visual
representation. To address this contradiction, we propose ALTA (ALign Through
Adapting), an efficient medical vision-language alignment method that utilizes
only about 8% of the trainable parameters and less than 1/5 of the
computational consumption required for masked record modeling. ALTA achieves
superior performance in vision-language matching tasks like retrieval and
zero-shot classification by adapting the pretrained vision model from masked
record modeling. Additionally, we integrate temporal-multiview radiograph
inputs to enhance the information consistency between radiographs and their
corresponding descriptions in reports, further improving the vision-language
alignment. Experimental evaluations show that ALTA outperforms the
best-performing counterpart by over 4% absolute points in text-to-image
accuracy and approximately 6% absolute points in image-to-text retrieval
accuracy. The adaptation of vision-language models during efficient alignment
also promotes better vision and language understanding. Code is publicly
available at https://github.com/DopamineLcy/ALTA. | 357 |
Accelerating Deep Learning with Shrinkage and Recall | Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorithm and screening technique used in LASSO, we propose a shrinking Deep
Learning with recall (sDLr) approach to speed up deep learning computation. We
experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network
(DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data
sets. Results show that the speedup using shrinking Deep Learning with recall
(sDLr) can reach more than 2.0 while still giving competitive classification
performance. | Disliked | zrz@andrew.cmu.edu | Accelerating Deep Learning with Shrinkage and Recall : Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorithm and screening technique used in LASSO, we propose a shrinking Deep
Learning with recall (sDLr) approach to speed up deep learning computation. We
experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network
(DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data
sets. Results show that the speedup using shrinking Deep Learning with recall
(sDLr) can reach more than 2.0 while still giving competitive classification
performance. | 0 | zrz@andrew.cmu.edu [SEP] Accelerating Deep Learning with Shrinkage and Recall : Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorithm and screening technique used in LASSO, we propose a shrinking Deep
Learning with recall (sDLr) approach to speed up deep learning computation. We
experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network
(DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data
sets. Results show that the speedup using shrinking Deep Learning with recall
(sDLr) can reach more than 2.0 while still giving competitive classification
performance. | 207 |
An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality in Machine Learning | We propose a clustering-based iterative algorithm to solve certain
optimization problems in machine learning, where we start the algorithm by
aggregating the original data, solving the problem on aggregated data, and then
in subsequent steps gradually disaggregate the aggregated data. We apply the
algorithm to common machine learning problems such as the least absolute
deviation regression problem, support vector machines, and semi-supervised
support vector machines. We derive model-specific data aggregation and
disaggregation procedures. We also show optimality, convergence, and the
optimality gap of the approximated solution in each iteration. A computational
study is provided. | Disliked | zrz@andrew.cmu.edu | An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality in Machine Learning : We propose a clustering-based iterative algorithm to solve certain
optimization problems in machine learning, where we start the algorithm by
aggregating the original data, solving the problem on aggregated data, and then
in subsequent steps gradually disaggregate the aggregated data. We apply the
algorithm to common machine learning problems such as the least absolute
deviation regression problem, support vector machines, and semi-supervised
support vector machines. We derive model-specific data aggregation and
disaggregation procedures. We also show optimality, convergence, and the
optimality gap of the approximated solution in each iteration. A computational
study is provided. | 0 | zrz@andrew.cmu.edu [SEP] An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality in Machine Learning : We propose a clustering-based iterative algorithm to solve certain
optimization problems in machine learning, where we start the algorithm by
aggregating the original data, solving the problem on aggregated data, and then
in subsequent steps gradually disaggregate the aggregated data. We apply the
algorithm to common machine learning problems such as the least absolute
deviation regression problem, support vector machines, and semi-supervised
support vector machines. We derive model-specific data aggregation and
disaggregation procedures. We also show optimality, convergence, and the
optimality gap of the approximated solution in each iteration. A computational
study is provided. | 65 |
Automated Machine Learning on Graphs: A Survey | Machine learning on graphs has been extensively studied in both academic and
industry. However, as the literature on graph learning booms with a vast number
of emerging methods and techniques, it becomes increasingly difficult to
manually design the optimal machine learning algorithm for different
graph-related tasks. To solve this critical challenge, automated machine
learning (AutoML) on graphs which combines the strength of graph machine
learning and AutoML together, is gaining attention from the research community.
Therefore, we comprehensively survey AutoML on graphs in this paper, primarily
focusing on hyper-parameter optimization (HPO) and neural architecture search
(NAS) for graph machine learning. We further overview libraries related to
automated graph machine learning and in-depth discuss AutoGL, the first
dedicated open-source library for AutoML on graphs. In the end, we share our
insights on future research directions for automated graph machine learning.
This paper is the first systematic and comprehensive review of automated
machine learning on graphs to the best of our knowledge. | Liked | zrz@andrew.cmu.edu | Automated Machine Learning on Graphs: A Survey : Machine learning on graphs has been extensively studied in both academic and
industry. However, as the literature on graph learning booms with a vast number
of emerging methods and techniques, it becomes increasingly difficult to
manually design the optimal machine learning algorithm for different
graph-related tasks. To solve this critical challenge, automated machine
learning (AutoML) on graphs which combines the strength of graph machine
learning and AutoML together, is gaining attention from the research community.
Therefore, we comprehensively survey AutoML on graphs in this paper, primarily
focusing on hyper-parameter optimization (HPO) and neural architecture search
(NAS) for graph machine learning. We further overview libraries related to
automated graph machine learning and in-depth discuss AutoGL, the first
dedicated open-source library for AutoML on graphs. In the end, we share our
insights on future research directions for automated graph machine learning.
This paper is the first systematic and comprehensive review of automated
machine learning on graphs to the best of our knowledge. | 1 | zrz@andrew.cmu.edu [SEP] Automated Machine Learning on Graphs: A Survey : Machine learning on graphs has been extensively studied in both academic and
industry. However, as the literature on graph learning booms with a vast number
of emerging methods and techniques, it becomes increasingly difficult to
manually design the optimal machine learning algorithm for different
graph-related tasks. To solve this critical challenge, automated machine
learning (AutoML) on graphs which combines the strength of graph machine
learning and AutoML together, is gaining attention from the research community.
Therefore, we comprehensively survey AutoML on graphs in this paper, primarily
focusing on hyper-parameter optimization (HPO) and neural architecture search
(NAS) for graph machine learning. We further overview libraries related to
automated graph machine learning and in-depth discuss AutoGL, the first
dedicated open-source library for AutoML on graphs. In the end, we share our
insights on future research directions for automated graph machine learning.
This paper is the first systematic and comprehensive review of automated
machine learning on graphs to the best of our knowledge. | 91 |
IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model | Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has
primarily been successful with single serial manipulators. For multi-arm
robotic systems, IK remains challenging due to complex self-collisions, coupled
joints, and high-dimensional redundancy. These complexities make traditional IK
solvers slow, prone to failure, and lacking in solution diversity. In this
paper, we present IKDiffuser, a diffusion-based model designed for fast and
diverse IK solution generation for multi-arm robotic systems. IKDiffuser learns
the joint distribution over the configuration space, capturing complex
dependencies and enabling seamless generalization to multi-arm robotic systems
of different structures. In addition, IKDiffuser can incorporate additional
objectives during inference without retraining, offering versatility and
adaptability for task-specific requirements. In experiments on 6 different
multi-arm systems, the proposed IKDiffuser achieves superior solution accuracy,
precision, diversity, and computational efficiency compared to existing
solvers. The proposed IKDiffuser framework offers a scalable, unified approach
to solving multi-arm IK problems, facilitating the potential of multi-arm
robotic systems in real-time manipulation tasks. | Liked | jechoi@andrew.cmu.edu | IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model : Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has
primarily been successful with single serial manipulators. For multi-arm
robotic systems, IK remains challenging due to complex self-collisions, coupled
joints, and high-dimensional redundancy. These complexities make traditional IK
solvers slow, prone to failure, and lacking in solution diversity. In this
paper, we present IKDiffuser, a diffusion-based model designed for fast and
diverse IK solution generation for multi-arm robotic systems. IKDiffuser learns
the joint distribution over the configuration space, capturing complex
dependencies and enabling seamless generalization to multi-arm robotic systems
of different structures. In addition, IKDiffuser can incorporate additional
objectives during inference without retraining, offering versatility and
adaptability for task-specific requirements. In experiments on 6 different
multi-arm systems, the proposed IKDiffuser achieves superior solution accuracy,
precision, diversity, and computational efficiency compared to existing
solvers. The proposed IKDiffuser framework offers a scalable, unified approach
to solving multi-arm IK problems, facilitating the potential of multi-arm
robotic systems in real-time manipulation tasks. | 1 | jechoi@andrew.cmu.edu [SEP] IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model : Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has
primarily been successful with single serial manipulators. For multi-arm
robotic systems, IK remains challenging due to complex self-collisions, coupled
joints, and high-dimensional redundancy. These complexities make traditional IK
solvers slow, prone to failure, and lacking in solution diversity. In this
paper, we present IKDiffuser, a diffusion-based model designed for fast and
diverse IK solution generation for multi-arm robotic systems. IKDiffuser learns
the joint distribution over the configuration space, capturing complex
dependencies and enabling seamless generalization to multi-arm robotic systems
of different structures. In addition, IKDiffuser can incorporate additional
objectives during inference without retraining, offering versatility and
adaptability for task-specific requirements. In experiments on 6 different
multi-arm systems, the proposed IKDiffuser achieves superior solution accuracy,
precision, diversity, and computational efficiency compared to existing
solvers. The proposed IKDiffuser framework offers a scalable, unified approach
to solving multi-arm IK problems, facilitating the potential of multi-arm
robotic systems in real-time manipulation tasks. | 406 |
Learning over time using a neuromorphic adaptive control algorithm for robotic arms | In this paper, we explore the ability of a robot arm to learn the underlying
operation space defined by the positions (x, y, z) that the arm's end-effector
can reach, including disturbances, by deploying and thoroughly evaluating a
Spiking Neural Network SNN-based adaptive control algorithm. While traditional
control algorithms for robotics have limitations in both adapting to new and
dynamic environments, we show that the robot arm can learn the operational
space and complete tasks faster over time. We also demonstrate that the
adaptive robot control algorithm based on SNNs enables a fast response while
maintaining energy efficiency. We obtained these results by performing an
extensive search of the adaptive algorithm parameter space, and evaluating
algorithm performance for different SNN network sizes, learning rates, dynamic
robot arm trajectories, and response times. We show that the robot arm learns
to complete tasks 15% faster in specific experiment scenarios such as scenarios
with six or nine random target points. | Liked | jechoi@andrew.cmu.edu | Learning over time using a neuromorphic adaptive control algorithm for robotic arms : In this paper, we explore the ability of a robot arm to learn the underlying
operation space defined by the positions (x, y, z) that the arm's end-effector
can reach, including disturbances, by deploying and thoroughly evaluating a
Spiking Neural Network SNN-based adaptive control algorithm. While traditional
control algorithms for robotics have limitations in both adapting to new and
dynamic environments, we show that the robot arm can learn the operational
space and complete tasks faster over time. We also demonstrate that the
adaptive robot control algorithm based on SNNs enables a fast response while
maintaining energy efficiency. We obtained these results by performing an
extensive search of the adaptive algorithm parameter space, and evaluating
algorithm performance for different SNN network sizes, learning rates, dynamic
robot arm trajectories, and response times. We show that the robot arm learns
to complete tasks 15% faster in specific experiment scenarios such as scenarios
with six or nine random target points. | 1 | jechoi@andrew.cmu.edu [SEP] Learning over time using a neuromorphic adaptive control algorithm for robotic arms : In this paper, we explore the ability of a robot arm to learn the underlying
operation space defined by the positions (x, y, z) that the arm's end-effector
can reach, including disturbances, by deploying and thoroughly evaluating a
Spiking Neural Network SNN-based adaptive control algorithm. While traditional
control algorithms for robotics have limitations in both adapting to new and
dynamic environments, we show that the robot arm can learn the operational
space and complete tasks faster over time. We also demonstrate that the
adaptive robot control algorithm based on SNNs enables a fast response while
maintaining energy efficiency. We obtained these results by performing an
extensive search of the adaptive algorithm parameter space, and evaluating
algorithm performance for different SNN network sizes, learning rates, dynamic
robot arm trajectories, and response times. We show that the robot arm learns
to complete tasks 15% faster in specific experiment scenarios such as scenarios
with six or nine random target points. | 414 |
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition | A deep learning approach has been widely applied in sequence modeling
problems. In terms of automatic speech recognition (ASR), its performance has
significantly been improved by increasing large speech corpus and deeper neural
network. Especially, recurrent neural network and deep convolutional neural
network have been applied in ASR successfully. Given the arising problem of
training speed, we build a novel deep recurrent convolutional network for
acoustic modeling and then apply deep residual learning to it. Our experiments
show that it has not only faster convergence speed but better recognition
accuracy over traditional deep convolutional recurrent network. In the
experiments, we compare the convergence speed of our novel deep recurrent
convolutional networks and traditional deep convolutional recurrent networks.
With faster convergence speed, our novel deep recurrent convolutional networks
can reach the comparable performance. We further show that applying deep
residual learning can boost the convergence speed of our novel deep recurret
convolutional networks. Finally, we evaluate all our experimental networks by
phoneme error rate (PER) with our proposed bidirectional statistical n-gram
language model. Our evaluation results show that our newly proposed deep
recurrent convolutional network applied with deep residual learning can reach
the best PER of 17.33\% with the fastest convergence speed on TIMIT database.
The outstanding performance of our novel deep recurrent convolutional neural
network with deep residual learning indicates that it can be potentially
adopted in other sequential problems. | Disliked | zrz@andrew.cmu.edu | Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition : A deep learning approach has been widely applied in sequence modeling
problems. In terms of automatic speech recognition (ASR), its performance has
significantly been improved by increasing large speech corpus and deeper neural
network. Especially, recurrent neural network and deep convolutional neural
network have been applied in ASR successfully. Given the arising problem of
training speed, we build a novel deep recurrent convolutional network for
acoustic modeling and then apply deep residual learning to it. Our experiments
show that it has not only faster convergence speed but better recognition
accuracy over traditional deep convolutional recurrent network. In the
experiments, we compare the convergence speed of our novel deep recurrent
convolutional networks and traditional deep convolutional recurrent networks.
With faster convergence speed, our novel deep recurrent convolutional networks
can reach the comparable performance. We further show that applying deep
residual learning can boost the convergence speed of our novel deep recurret
convolutional networks. Finally, we evaluate all our experimental networks by
phoneme error rate (PER) with our proposed bidirectional statistical n-gram
language model. Our evaluation results show that our newly proposed deep
recurrent convolutional network applied with deep residual learning can reach
the best PER of 17.33\% with the fastest convergence speed on TIMIT database.
The outstanding performance of our novel deep recurrent convolutional neural
network with deep residual learning indicates that it can be potentially
adopted in other sequential problems. | 0 | zrz@andrew.cmu.edu [SEP] Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition : A deep learning approach has been widely applied in sequence modeling
problems. In terms of automatic speech recognition (ASR), its performance has
significantly been improved by increasing large speech corpus and deeper neural
network. Especially, recurrent neural network and deep convolutional neural
network have been applied in ASR successfully. Given the arising problem of
training speed, we build a novel deep recurrent convolutional network for
acoustic modeling and then apply deep residual learning to it. Our experiments
show that it has not only faster convergence speed but better recognition
accuracy over traditional deep convolutional recurrent network. In the
experiments, we compare the convergence speed of our novel deep recurrent
convolutional networks and traditional deep convolutional recurrent networks.
With faster convergence speed, our novel deep recurrent convolutional networks
can reach the comparable performance. We further show that applying deep
residual learning can boost the convergence speed of our novel deep recurret
convolutional networks. Finally, we evaluate all our experimental networks by
phoneme error rate (PER) with our proposed bidirectional statistical n-gram
language model. Our evaluation results show that our newly proposed deep
recurrent convolutional network applied with deep residual learning can reach
the best PER of 17.33\% with the fastest convergence speed on TIMIT database.
The outstanding performance of our novel deep recurrent convolutional neural
network with deep residual learning indicates that it can be potentially
adopted in other sequential problems. | 221 |
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches | Despite the recent success of deep transfer learning approaches in NLP, there
is a lack of quantitative studies demonstrating the gains these models offer in
low-shot text classification tasks over existing paradigms. Deep transfer
learning approaches such as BERT and ULMFiT demonstrate that they can beat
state-of-the-art results on larger datasets, however when one has only 100-1000
labelled examples per class, the choice of approach is less clear, with
classical machine learning and deep transfer learning representing valid
options. This paper compares the current best transfer learning approach with
top classical machine learning approaches on a trinary sentiment classification
task to assess the best paradigm. We find that BERT, representing the best of
deep transfer learning, is the best performing approach, outperforming top
classical machine learning algorithms by 9.7% on average when trained with 100
examples per class, narrowing to 1.8% at 1000 labels per class. We also show
the robustness of deep transfer learning in moving across domains, where the
maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross
domain, compared to classical machine learning which loses up to 20.6%. | Disliked | zrz@andrew.cmu.edu | Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches : Despite the recent success of deep transfer learning approaches in NLP, there
is a lack of quantitative studies demonstrating the gains these models offer in
low-shot text classification tasks over existing paradigms. Deep transfer
learning approaches such as BERT and ULMFiT demonstrate that they can beat
state-of-the-art results on larger datasets, however when one has only 100-1000
labelled examples per class, the choice of approach is less clear, with
classical machine learning and deep transfer learning representing valid
options. This paper compares the current best transfer learning approach with
top classical machine learning approaches on a trinary sentiment classification
task to assess the best paradigm. We find that BERT, representing the best of
deep transfer learning, is the best performing approach, outperforming top
classical machine learning algorithms by 9.7% on average when trained with 100
examples per class, narrowing to 1.8% at 1000 labels per class. We also show
the robustness of deep transfer learning in moving across domains, where the
maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross
domain, compared to classical machine learning which loses up to 20.6%. | 0 | zrz@andrew.cmu.edu [SEP] Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches : Despite the recent success of deep transfer learning approaches in NLP, there
is a lack of quantitative studies demonstrating the gains these models offer in
low-shot text classification tasks over existing paradigms. Deep transfer
learning approaches such as BERT and ULMFiT demonstrate that they can beat
state-of-the-art results on larger datasets, however when one has only 100-1000
labelled examples per class, the choice of approach is less clear, with
classical machine learning and deep transfer learning representing valid
options. This paper compares the current best transfer learning approach with
top classical machine learning approaches on a trinary sentiment classification
task to assess the best paradigm. We find that BERT, representing the best of
deep transfer learning, is the best performing approach, outperforming top
classical machine learning algorithms by 9.7% on average when trained with 100
examples per class, narrowing to 1.8% at 1000 labels per class. We also show
the robustness of deep transfer learning in moving across domains, where the
maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross
domain, compared to classical machine learning which loses up to 20.6%. | 263 |
Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement | The emergence of 6G wireless networks promises to revolutionize vehicular
communications by enabling ultra-reliable, low-latency, and high-capacity data
exchange. In this context, collaborative perception techniques, where multiple
vehicles or infrastructure nodes cooperate to jointly receive and decode
transmitted signals, aim to enhance reliability and spectral efficiency for
Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an
end-to-end wireless neural receiver based on a Differential Transformer
architecture, tailored for 6G V2X communication with a specific focus on
enabling collaborative perception among connected autonomous vehicles. Our
model integrates key components of the 6G physical layer, designed to boost
performance in dynamic and challenging autonomous driving environments. We
validate the proposed system across a range of scenarios, including
3GPP-defined Urban Macro (UMa) channel. To assess the model's real-world
applicability, we evaluate its robustness within a V2X framework. In a
collaborative perception scenario, our system processes heterogeneous LiDAR and
camera data from four connected vehicles in dynamic cooperative vehicular
networks. The results show significant improvements over state-of-the-art
methods, achieving an average precision of 0.84, highlighting the potential of
our proposed approach to enable robust, intelligent, and adaptive wireless
cooperation for next-generation connected autonomous vehicles. | Disliked | zrz@andrew.cmu.edu | Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement : The emergence of 6G wireless networks promises to revolutionize vehicular
communications by enabling ultra-reliable, low-latency, and high-capacity data
exchange. In this context, collaborative perception techniques, where multiple
vehicles or infrastructure nodes cooperate to jointly receive and decode
transmitted signals, aim to enhance reliability and spectral efficiency for
Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an
end-to-end wireless neural receiver based on a Differential Transformer
architecture, tailored for 6G V2X communication with a specific focus on
enabling collaborative perception among connected autonomous vehicles. Our
model integrates key components of the 6G physical layer, designed to boost
performance in dynamic and challenging autonomous driving environments. We
validate the proposed system across a range of scenarios, including
3GPP-defined Urban Macro (UMa) channel. To assess the model's real-world
applicability, we evaluate its robustness within a V2X framework. In a
collaborative perception scenario, our system processes heterogeneous LiDAR and
camera data from four connected vehicles in dynamic cooperative vehicular
networks. The results show significant improvements over state-of-the-art
methods, achieving an average precision of 0.84, highlighting the potential of
our proposed approach to enable robust, intelligent, and adaptive wireless
cooperation for next-generation connected autonomous vehicles. | 0 | zrz@andrew.cmu.edu [SEP] Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement : The emergence of 6G wireless networks promises to revolutionize vehicular
communications by enabling ultra-reliable, low-latency, and high-capacity data
exchange. In this context, collaborative perception techniques, where multiple
vehicles or infrastructure nodes cooperate to jointly receive and decode
transmitted signals, aim to enhance reliability and spectral efficiency for
Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an
end-to-end wireless neural receiver based on a Differential Transformer
architecture, tailored for 6G V2X communication with a specific focus on
enabling collaborative perception among connected autonomous vehicles. Our
model integrates key components of the 6G physical layer, designed to boost
performance in dynamic and challenging autonomous driving environments. We
validate the proposed system across a range of scenarios, including
3GPP-defined Urban Macro (UMa) channel. To assess the model's real-world
applicability, we evaluate its robustness within a V2X framework. In a
collaborative perception scenario, our system processes heterogeneous LiDAR and
camera data from four connected vehicles in dynamic cooperative vehicular
networks. The results show significant improvements over state-of-the-art
methods, achieving an average precision of 0.84, highlighting the potential of
our proposed approach to enable robust, intelligent, and adaptive wireless
cooperation for next-generation connected autonomous vehicles. | 328 |
A Survey on Deep Learning for Skeleton-Based Human Animation | Human character animation is often critical in entertainment content
production, including video games, virtual reality or fiction films. To this
end, deep neural networks drive most recent advances through deep learning and
deep reinforcement learning. In this article, we propose a comprehensive survey
on the state-of-the-art approaches based on either deep learning or deep
reinforcement learning in skeleton-based human character animation. First, we
introduce motion data representations, most common human motion datasets and
how basic deep models can be enhanced to foster learning of spatial and
temporal patterns in motion data. Second, we cover state-of-the-art approaches
divided into three large families of applications in human animation pipelines:
motion synthesis, character control and motion editing. Finally, we discuss the
limitations of the current state-of-the-art methods based on deep learning
and/or deep reinforcement learning in skeletal human character animation and
possible directions of future research to alleviate current limitations and
meet animators' needs. | Liked | zrz@andrew.cmu.edu | A Survey on Deep Learning for Skeleton-Based Human Animation : Human character animation is often critical in entertainment content
production, including video games, virtual reality or fiction films. To this
end, deep neural networks drive most recent advances through deep learning and
deep reinforcement learning. In this article, we propose a comprehensive survey
on the state-of-the-art approaches based on either deep learning or deep
reinforcement learning in skeleton-based human character animation. First, we
introduce motion data representations, most common human motion datasets and
how basic deep models can be enhanced to foster learning of spatial and
temporal patterns in motion data. Second, we cover state-of-the-art approaches
divided into three large families of applications in human animation pipelines:
motion synthesis, character control and motion editing. Finally, we discuss the
limitations of the current state-of-the-art methods based on deep learning
and/or deep reinforcement learning in skeletal human character animation and
possible directions of future research to alleviate current limitations and
meet animators' needs. | 1 | zrz@andrew.cmu.edu [SEP] A Survey on Deep Learning for Skeleton-Based Human Animation : Human character animation is often critical in entertainment content
production, including video games, virtual reality or fiction films. To this
end, deep neural networks drive most recent advances through deep learning and
deep reinforcement learning. In this article, we propose a comprehensive survey
on the state-of-the-art approaches based on either deep learning or deep
reinforcement learning in skeleton-based human character animation. First, we
introduce motion data representations, most common human motion datasets and
how basic deep models can be enhanced to foster learning of spatial and
temporal patterns in motion data. Second, we cover state-of-the-art approaches
divided into three large families of applications in human animation pipelines:
motion synthesis, character control and motion editing. Finally, we discuss the
limitations of the current state-of-the-art methods based on deep learning
and/or deep reinforcement learning in skeletal human character animation and
possible directions of future research to alleviate current limitations and
meet animators' needs. | 229 |
A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks | This paper develops a planner that plans the action sequences and motion for
a dual-arm robot to lift up and flip heavy plates using crane pulley blocks.
The problem is motivated by the low payload of modern collaborative robots.
Instead of directly manipulating heavy plates that collaborative robots cannot
afford, the paper develops a planner for collaborative robots to operate crane
pulley blocks. The planner assumes a target plate is pre-attached to the crane
hook. It optimizes dual-arm action sequences and plans the robot's dual-arm
motion that pulls the rope of the crane pulley blocks to lift up the plate. The
crane pulley blocks reduce the payload that each robotic arm needs to bear.
When the plate is lifted up to a satisfying pose, the planner plans a pushing
motion for one of the robot arms to tumble over the plate while considering
force and moment constraints. The article presents the technical details of the
planner and several experiments and analysis carried out using a dual-arm robot
made by two Universal Robots UR3 arms. The influence of various parameters and
optimization goals are investigated and compared in depth. The results show
that the proposed planner is flexible and efficient. | Disliked | jechoi@andrew.cmu.edu | A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks : This paper develops a planner that plans the action sequences and motion for
a dual-arm robot to lift up and flip heavy plates using crane pulley blocks.
The problem is motivated by the low payload of modern collaborative robots.
Instead of directly manipulating heavy plates that collaborative robots cannot
afford, the paper develops a planner for collaborative robots to operate crane
pulley blocks. The planner assumes a target plate is pre-attached to the crane
hook. It optimizes dual-arm action sequences and plans the robot's dual-arm
motion that pulls the rope of the crane pulley blocks to lift up the plate. The
crane pulley blocks reduce the payload that each robotic arm needs to bear.
When the plate is lifted up to a satisfying pose, the planner plans a pushing
motion for one of the robot arms to tumble over the plate while considering
force and moment constraints. The article presents the technical details of the
planner and several experiments and analysis carried out using a dual-arm robot
made by two Universal Robots UR3 arms. The influence of various parameters and
optimization goals are investigated and compared in depth. The results show
that the proposed planner is flexible and efficient. | 0 | jechoi@andrew.cmu.edu [SEP] A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks : This paper develops a planner that plans the action sequences and motion for
a dual-arm robot to lift up and flip heavy plates using crane pulley blocks.
The problem is motivated by the low payload of modern collaborative robots.
Instead of directly manipulating heavy plates that collaborative robots cannot
afford, the paper develops a planner for collaborative robots to operate crane
pulley blocks. The planner assumes a target plate is pre-attached to the crane
hook. It optimizes dual-arm action sequences and plans the robot's dual-arm
motion that pulls the rope of the crane pulley blocks to lift up the plate. The
crane pulley blocks reduce the payload that each robotic arm needs to bear.
When the plate is lifted up to a satisfying pose, the planner plans a pushing
motion for one of the robot arms to tumble over the plate while considering
force and moment constraints. The article presents the technical details of the
planner and several experiments and analysis carried out using a dual-arm robot
made by two Universal Robots UR3 arms. The influence of various parameters and
optimization goals are investigated and compared in depth. The results show
that the proposed planner is flexible and efficient. | 391 |
A Survey Analyzing Generalization in Deep Reinforcement Learning | Reinforcement learning research obtained significant success and attention
with the utilization of deep neural networks to solve problems in high
dimensional state or action spaces. While deep reinforcement learning policies
are currently being deployed in many different fields from medical applications
to large language models, there are still ongoing questions the field is trying
to answer on the generalization capabilities of deep reinforcement learning
policies. In this paper, we will formalize and analyze generalization in deep
reinforcement learning. We will explain the fundamental reasons why deep
reinforcement learning policies encounter overfitting problems that limit their
generalization capabilities. Furthermore, we will categorize and explain the
manifold solution approaches to increase generalization, and overcome
overfitting in deep reinforcement learning policies. From exploration to
adversarial analysis and from regularization to robustness our paper provides
an analysis on a wide range of subfields within deep reinforcement learning
with a broad scope and in-depth view. We believe our study can provide a
compact guideline for the current advancements in deep reinforcement learning,
and help to construct robust deep neural policies with higher generalization
skills. | Liked | zrz@andrew.cmu.edu | A Survey Analyzing Generalization in Deep Reinforcement Learning : Reinforcement learning research obtained significant success and attention
with the utilization of deep neural networks to solve problems in high
dimensional state or action spaces. While deep reinforcement learning policies
are currently being deployed in many different fields from medical applications
to large language models, there are still ongoing questions the field is trying
to answer on the generalization capabilities of deep reinforcement learning
policies. In this paper, we will formalize and analyze generalization in deep
reinforcement learning. We will explain the fundamental reasons why deep
reinforcement learning policies encounter overfitting problems that limit their
generalization capabilities. Furthermore, we will categorize and explain the
manifold solution approaches to increase generalization, and overcome
overfitting in deep reinforcement learning policies. From exploration to
adversarial analysis and from regularization to robustness our paper provides
an analysis on a wide range of subfields within deep reinforcement learning
with a broad scope and in-depth view. We believe our study can provide a
compact guideline for the current advancements in deep reinforcement learning,
and help to construct robust deep neural policies with higher generalization
skills. | 1 | zrz@andrew.cmu.edu [SEP] A Survey Analyzing Generalization in Deep Reinforcement Learning : Reinforcement learning research obtained significant success and attention
with the utilization of deep neural networks to solve problems in high
dimensional state or action spaces. While deep reinforcement learning policies
are currently being deployed in many different fields from medical applications
to large language models, there are still ongoing questions the field is trying
to answer on the generalization capabilities of deep reinforcement learning
policies. In this paper, we will formalize and analyze generalization in deep
reinforcement learning. We will explain the fundamental reasons why deep
reinforcement learning policies encounter overfitting problems that limit their
generalization capabilities. Furthermore, we will categorize and explain the
manifold solution approaches to increase generalization, and overcome
overfitting in deep reinforcement learning policies. From exploration to
adversarial analysis and from regularization to robustness our paper provides
an analysis on a wide range of subfields within deep reinforcement learning
with a broad scope and in-depth view. We believe our study can provide a
compact guideline for the current advancements in deep reinforcement learning,
and help to construct robust deep neural policies with higher generalization
skills. | 174 |
Manipulability optimization for multi-arm teleoperation | Teleoperation provides a way for human operators to guide robots in
situations where full autonomy is challenging or where direct human
intervention is required. It can also be an important tool to teach robots in
order to achieve autonomous behaviour later on. The increased availability of
collaborative robot arms and Virtual Reality (VR) devices provides ample
opportunity for development of novel teleoperation methods. Since robot arms
are often kinematically different from human arms, mapping human motions to a
robot in real-time is not trivial. Additionally, a human operator might steer
the robot arm toward singularities or its workspace limits, which can lead to
undesirable behaviour. This is further accentuated for the orchestration of
multiple robots. In this paper, we present a VR interface targeted to multi-arm
payload manipulation, which can closely match real-time input motion. Allowing
the user to manipulate the payload rather than mapping their motions to
individual arms we are able to simultaneously guide multiple collaborative
arms. By releasing a single rotational degree of freedom, and by using a local
optimization method, we can improve each arm's manipulability index, which in
turn lets us avoid kinematic singularities and workspace limitations. We apply
our approach to predefined trajectories as well as real-time teleoperation on
different robot arms and compare performance in terms of end effector position
error and relevant joint motion metrics. | Liked | jechoi@andrew.cmu.edu | Manipulability optimization for multi-arm teleoperation : Teleoperation provides a way for human operators to guide robots in
situations where full autonomy is challenging or where direct human
intervention is required. It can also be an important tool to teach robots in
order to achieve autonomous behaviour later on. The increased availability of
collaborative robot arms and Virtual Reality (VR) devices provides ample
opportunity for development of novel teleoperation methods. Since robot arms
are often kinematically different from human arms, mapping human motions to a
robot in real-time is not trivial. Additionally, a human operator might steer
the robot arm toward singularities or its workspace limits, which can lead to
undesirable behaviour. This is further accentuated for the orchestration of
multiple robots. In this paper, we present a VR interface targeted to multi-arm
payload manipulation, which can closely match real-time input motion. Allowing
the user to manipulate the payload rather than mapping their motions to
individual arms we are able to simultaneously guide multiple collaborative
arms. By releasing a single rotational degree of freedom, and by using a local
optimization method, we can improve each arm's manipulability index, which in
turn lets us avoid kinematic singularities and workspace limitations. We apply
our approach to predefined trajectories as well as real-time teleoperation on
different robot arms and compare performance in terms of end effector position
error and relevant joint motion metrics. | 1 | jechoi@andrew.cmu.edu [SEP] Manipulability optimization for multi-arm teleoperation : Teleoperation provides a way for human operators to guide robots in
situations where full autonomy is challenging or where direct human
intervention is required. It can also be an important tool to teach robots in
order to achieve autonomous behaviour later on. The increased availability of
collaborative robot arms and Virtual Reality (VR) devices provides ample
opportunity for development of novel teleoperation methods. Since robot arms
are often kinematically different from human arms, mapping human motions to a
robot in real-time is not trivial. Additionally, a human operator might steer
the robot arm toward singularities or its workspace limits, which can lead to
undesirable behaviour. This is further accentuated for the orchestration of
multiple robots. In this paper, we present a VR interface targeted to multi-arm
payload manipulation, which can closely match real-time input motion. Allowing
the user to manipulate the payload rather than mapping their motions to
individual arms we are able to simultaneously guide multiple collaborative
arms. By releasing a single rotational degree of freedom, and by using a local
optimization method, we can improve each arm's manipulability index, which in
turn lets us avoid kinematic singularities and workspace limitations. We apply
our approach to predefined trajectories as well as real-time teleoperation on
different robot arms and compare performance in terms of end effector position
error and relevant joint motion metrics. | 14 |
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