pwc-archive/papers-with-abstracts huggingface dataset:
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell
Authors: Mohammed Sharafath Abdul Hameed, Md Muzahid Khan, Andreas Schwung
Date: 2020-11-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

This paper introduces a novel combination of scheduling control on a flexible robot manufacturing cell with curiosity based reinforcement learning. Reinforcement learning has proved to be highly successful in solving tasks like robotics and scheduling. But this requires hand tuning of rewards in problem domains like robotics and scheduling even where the solution is not obvious. To this end, we apply a curiosity based reinforcement learning, using intrinsic motivation as a form of reward, on a flexible robot manufacturing cell to alleviate this problem. Further, the learning agents are embedded into the transportation robots to enable a generalized learning solution that can be applied to a variety of environments. In the first approach, the curiosity based reinforcement learning is applied to a simple structured robot manufacturing cell. And in the second approach, the same algorithm is applied to a graph structured robot manufacturing cell. Results from the experiments show that the agents are able to solve both the environments with the ability to transfer the curiosity module directly from one environment to another. We conclude that curiosity based learning on scheduling tasks provide a viable alternative to the reward shaped reinforcement learning traditionally used.
Global Road Damage Detection: State-of-the-art Solutions
Authors: Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Hiroshi Omata, Takehiro Kashiyama, Yoshihide Sekimoto
Date: 2020-11-17 00:00:00
Tasks: Ensemble Learning, Road Damage Detection

This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges.
Using Explainable Scheduling for the Mars 2020 Rover Mission
Authors: Jagriti Agrawal, Amruta Yelamanchili, Steve Chien
Date: 2020-11-17 00:00:00
Tasks: Scheduling

Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.
Predicting Rigid Body Dynamics using Dual Quaternion Recurrent Neural Networks with Quaternion Attention
Authors: Johannes Pöppelbaum, Andreas Schwung
Date: 2020-11-17 00:00:00
Tasks: Position

We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of informations with a main focus on describing rigid body movements. To cover the dynamic behavior inherent to rigid body movements, we propose recurrent architectures in the neural network. To further model the interactions between individual rigid bodies as well as external inputs efficiently, we incorporate a novel attention mechanism employing dual quaternion algebra. The introduced architecture is trainable by means of gradient based algorithms. We apply our approach to a parcel prediction problem where a rigid body with an initial position, orientation, velocity and angular velocity moves through a fixed simulation environment which exhibits rich interactions between the parcel and the boundaries.
Fault-Aware Robust Control via Adversarial Reinforcement Learning
Authors: Fan Yang, Chao Yang, Di Guo, Huaping Liu, Fuchun Sun
Date: 2020-11-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Robots have limited adaptation ability compared to humans and animals in the case of damage. However, robot damages are prevalent in real-world applications, especially for robots deployed in extreme environments. The fragility of robots greatly limits their widespread application. We propose an adversarial reinforcement learning framework, which significantly increases robot robustness over joint damage cases in both manipulation tasks and locomotion tasks. The agent is trained iteratively under the joint damage cases where it has poor performance. We validate our algorithm on a three-fingered robot hand and a quadruped robot. Our algorithm can be trained only in simulation and directly deployed on a real robot without any fine-tuning. It also demonstrates exceeding success rates over arbitrary joint damage cases.
Modality-Buffet for Real-Time Object Detection
Authors: Nicolai Dorka, Johannes Meyer, Wolfram Burgard
Date: 2020-11-17 00:00:00
Tasks: Decision Making, Object, object-detection, Object Detection, Real-Time Object Detection, Reinforcement Learning (RL), Sequential Decision Making

Real-time object detection in videos using lightweight hardware is a crucial component of many robotic tasks. Detectors using different modalities and with varying computational complexities offer different trade-offs. One option is to have a very lightweight model that can predict from all modalities at once for each frame. However, in some situations (e.g., in static scenes) it might be better to have a more complex but more accurate model and to extrapolate from previous predictions for the frames coming in at processing time. We formulate this task as a sequential decision making problem and use reinforcement learning (RL) to generate a policy that decides from the RGB input which detector out of a portfolio of different object detectors to take for the next prediction. The objective of the RL agent is to maximize the accuracy of the predictions per image. We evaluate the approach on the Waymo Open Dataset and show that it exceeds the performance of each single detector.
RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
Authors: Videsh Suman, Phu Pham, Aniket Bera
Date: 2020-11-17 00:00:00

A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. An intuitive approach to enabling an intelligent automated driving system would be incorporating some aspects of human driving behavior. To this end, we propose a novel driving framework for egocentric views based on spatio-temporal traffic graphs. The traffic graphs model not only the spatial interactions amongst the road users but also their individual intentions through temporally associated message passing. We leverage a spatio-temporal graph convolutional network (ST-GCN) to train the graph edges. These edges are formulated using parameterized functions of 3D positions and scene-aware appearance features of road agents. Along with tactical behavior prediction, it is crucial to evaluate the risk-assessing ability of the proposed framework. We claim that our framework learns risk-aware representations by improving on the task of risk object identification, especially in identifying objects with vulnerable interactions like pedestrians and cyclists.
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders
Authors: Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal
Date: 2020-11-17 00:00:00
Tasks: Classification, General Classification, Morphology classification

The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the ever growing set of unlabelled images, semi-supervised learning could be an effective way of reducing the required labelling and increasing classification accuracy. We develop a Variational Autoencoder (VAE) with Equivariant Transformer layers with a classifier network from the latent space. We show that this novel architecture leads to improvements in accuracy when used for the galaxy morphology classification task on the Galaxy Zoo data set. In addition we show that pre-training the classifier network as part of the VAE using the unlabelled data leads to higher accuracy with fewer labels compared to exiting approaches. This novel VAE has the potential to automate galaxy morphology classification with reduced human labelling efforts.
A Simple Framework to Quantify Different Types of Uncertainty in Deep Neural Networks for Image Classification
Authors: Aria Khoshsirat
Date: 2020-11-17 00:00:00
Tasks: General Classification, image-classification, Image Classification, Medical Image Analysis

Quantifying uncertainty in a model's predictions is important as it enables the safety of an AI system to be increased by acting on the model's output in an informed manner. This is crucial for applications where the cost of an error is high, such as in autonomous vehicle control, medical image analysis, financial estimations or legal fields. Deep Neural Networks are powerful predictors that have recently achieved state-of-the-art performance on a wide spectrum of tasks. Quantifying predictive uncertainty in DNNs is a challenging and yet on-going problem. In this paper we propose a complete framework to capture and quantify three known types of uncertainty in DNNs for the task of image classification. This framework includes an ensemble of CNNs for model uncertainty, a supervised reconstruction auto-encoder to capture distributional uncertainty and using the output of activation functions in the last layer of the network, to capture data uncertainty. Finally we demonstrate the efficiency of our method on popular image datasets for classification.
VIB is Half Bayes
Authors: Alexander A Alemi, Warren R Morningstar, Ben Poole, Ian Fischer, Joshua V Dillon
Date: 2020-11-17 00:00:00
Proceeding: pproximateinference AABI Symposium 2021 1
Tasks: regression

In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational Information Bottleneck can be viewed as a compromise between fully empirical and fully Bayesian objectives, attempting to minimize the risks due to finite sampling of Y only. We argue that this approach provides some of the benefits of Bayes while requiring only some of the work.
Reactive Long Horizon Task Execution via Visual Skill and Precondition Models
Authors: Shohin Mukherjee, Chris Paxton, Arsalan Mousavian, Adam Fishman, Maxim Likhachev, Dieter Fox
Date: 2020-11-17 00:00:00

Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible. We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We learn a library of parameterized skills, along with a set of predicates-based preconditions and termination conditions, entirely in simulation. We explore a block-stacking task because it has a clear structure, where multiple skills must be chained together, but our methods are applicable to a wide range of other problems and domains, and can transfer from simulation to the real-world with no fine tuning. The system is able to recognize failures and accomplish long-horizon tasks from perceptual input, which is critical for real-world execution. We evaluate our proposed approach in both simulation and in the real-world, showing an increase in success rate from 91.6% to 98% in simulation and from 10% to 80% success rate in the real-world as compared with naive baselines. For experiment videos including both real-world and simulation, see: https://www.youtube.com/playlist?list=PL-oD0xHUngeLfQmpngYkGFZarstfPOXqX
Pyramid Point: A Multi-Level Focusing Network for Revisiting Feature Layers
Authors: Nina Varney, Vijayan K. Asari, Quinn Graehling
Date: 2020-11-17 00:00:00
Tasks: Semantic Segmentation

We present a method to learn a diverse group of object categories from an unordered point set. We propose our Pyramid Point network, which uses a dense pyramid structure instead of the traditional 'U' shape, typically seen in semantic segmentation networks. This pyramid structure gives a second look, allowing the network to revisit different layers simultaneously, increasing the contextual information by creating additional layers with less noise. We introduce a Focused Kernel Point convolution (FKP Conv), which expands on the traditional point convolutions by adding an attention mechanism to the kernel outputs. This FKP Conv increases our feature quality and allows us to weigh the kernel outputs dynamically. These FKP Convs are the central part of our Recurrent FKP Bottleneck block, which makes up the backbone of our encoder. With this distinct network, we demonstrate competitive performance on three benchmark data sets. We also perform an ablation study to show the positive effects of each element in our FKP Conv.
Developing an Effective and Automated Patient Engagement Estimator for Telehealth: A Machine Learning Approach
Authors: Pooja Guhan, Naman Awasthi, and Kathryn McDonald, Kristin Bussell, Dinesh Manocha, Gloria Reeves, Aniket Bera
Date: 2020-11-17 00:00:00
Tasks: regression

We discuss MET, a learning-based algorithm proposed for perceiving a patient's level of engagement during telehealth sessions. We leverage latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature to understand a person's level of engagement in a semi-supervised GAN-based framework. We showcase the efficacy of this method from the perspective of mental health and more specifically how this can be leveraged for a better understanding of patient engagement during telemental health sessions. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1299 video clips, each 3 seconds long and show experiments on the same. Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests, we also observed positive correlations between the working alliance inventory scores reported by psychotherapists. This indicates the potential of the proposed model to present patient engagement estimations that aligns well with the engagement measures used by psychotherapists.
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation
Authors: Venkatraman Narayanan, Bala Murali Manoghar, Rama Prashanth RV, Phu Pham, Aniket Bera
Date: 2020-11-17 00:00:00
Tasks: Human Detection, Human Instance Segmentation, Instance Segmentation, reinforcement-learning, Reinforcement Learning (RL), Semantic Segmentation

Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic obstacles. We propose SeekNet, an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded pedestrians. We also demonstrate the benefits of our algorithm on occluded human detection and tracking over other baselines. Additionally, we set up a multi-robot environment with SeekNet to identify and track visual disease markers for airborne disease in crowded areas. We conduct our experiments in a simulated indoor environment and show that our method enhances the overall accuracy of the amodal recognition task and achieves the largest improvement in detection accuracy over time in comparison to the baseline approaches.
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Authors: Sicheng Zhao, Yang Xiao, Jiang Guo, Xiangyu Yue, Jufeng Yang, Ravi Krishna, Pengfei Xu, Kurt Keutzer
Date: 2020-11-17 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network, Sentiment Analysis, Sentiment Classification

Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate large-scale annotations on the target domain, domain adaptation (DA) provides an alternate solution by learning a transferable model from other labeled source domains. Existing multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources and the distribution difference among different sub-domains even in the same source, or cannot reflect the varying optimal weighting during different training stages. In this paper, we propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN), to address the above issues. Specifically, C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features. Further, our dynamic instance-level weighting mechanisms can assign the optimal weights to different source samples in each training stage. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art DA approaches. Our source code is released at: https://github.com/WArushrush/Curriculum-CycleGAN.
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting
Authors: Zhigang Jia, Qiyu Jin, Michael K. Ng, XiLe Zhao
Date: 2020-11-17 00:00:00
Tasks: Matrix Completion, SSIM, Video Inpainting

The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in many recently proposed machining learning algorithms for image processing. However, there is no theoretical analysis on its working principle in the literature. In this paper, we discover a potential causality between NSS and low-rank property of color images, which is also available to grey images. A new patch group based NSS prior scheme is proposed to learn explicit NSS models of natural color images. The numerical low-rank property of patched matrices is also rigorously proved. The NSS-based QMC algorithm computes an optimal low-rank approximation to the high-rank color image, resulting in high PSNR and SSIM measures and particularly the better visual quality. A new tensor NSS-based QMC method is also presented to solve the color video inpainting problem based on quaternion tensor representation. The numerical experiments on color images and videos indicate the advantages of NSS-based QMC over the state-of-the-art methods.
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
Authors: Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
Date: 2020-11-17 00:00:00

We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We obtain the scaled cumulant-generating function for the dynamical activity of the Fredrickson-Andersen model, a prototypical kinetically constrained model, in one and two dimensions, and present the first size-scaling analysis of the dynamical activity in two dimensions. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.
3D CNNs with Adaptive Temporal Feature Resolutions
Authors: Mohsen Fayyaz, Emad Bahrami, Ali Diba, Mehdi Noroozi, Ehsan Adeli, Luc van Gool, Juergen Gall
Date: 2020-11-17 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Recognition

While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not anymore static but it varies for each input video clip. By integrating SGS as an additional layer within current 3D CNNs, we can convert them into much more efficient 3D CNNs with adaptive temporal feature resolutions (ATFR). Our evaluations show that the proposed module improves the state-of-the-art by reducing the computational cost (GFLOPs) by half while preserving or even improving the accuracy. We evaluate our module by adding it to multiple state-of-the-art 3D CNNs on various datasets such as Kinetics-600, Kinetics-400, mini-Kinetics, Something-Something V2, UCF101, and HMDB51.