Reviewerly/exHarmony huggingface dataset:
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving

Outdoor 3D object detection has played an essential role in the environment perception of autonomous driving. In complicated traffic situations, precise object recognition provides indispensable information for prediction and planning in the dynamic system, improving self-driving safety and reliability. However, with the vehicle's veering, the constant rotation of the surrounding scenario makes a challenge for the perception systems. Yet most existing methods have not focused on alleviating the detection accuracy impairment brought by the vehicle's rotation, especially in outdoor 3D detection. In this paper, we propose DuEqNet, which first introduces the concept of equivariance into 3D object detection network by leveraging a hierarchical embedded framework. The dual-equivariance of our model can extract the equivariant features at both local and global levels, respectively. For the local feature, we utilize the graph-based strategy to guarantee the equivariance of the feature in point cloud pillars. In terms of the global feature, the group equivariant convolution layers are adopted to aggregate the local feature to achieve the global equivariance. In the experiment part, we evaluate our approach with different baselines in 3D object detection tasks and obtain State-Of-The-Art performance. According to the results, our model presents higher accuracy on orientation and better prediction efficiency. Moreover, our dual-equivariance strategy exhibits the satisfied plug-and-play ability on various popular object detection frameworks to improve their performance.
Efficient Inference of Temporal Task Specifications from Human Demonstrations using Experiment Design

Robotic deployments in human environments have motivated the need for autonomous systems to be able to interact with humans and solve tasks effectively. Human demonstrations of tasks can be used to infer underlying task specifications, commonly modeled with temporal logic. State-of-the-art methods have developed Bayesian inference tools to estimate a temporal logic formula from a sequence of demon-strations. The current work proposes the use of experiment design to choose environments for humans to perform these demonstrations. This reduces the number of demonstrations needed to estimate the unknown ground truth formula with low error. A novel computationally efficient strategy is proposed to generate informative environments by using an optimal planner as the model for the demonstrator. Instead of evaluating all possible environments, the search space reduces to the placement of informative orderings of likely eventual goals along an optimal planner's solution. A human study with 600 demonstrations from 20 participants for 4 tasks on a 2D interface validates the proposed hypothesis and empirical performance benefit in terms of convergence and error over baselines. The human study dataset is also publicly shared.
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN

Object recognition and instance segmentation are fundamental skills in any robotic or autonomous system. Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications. In this paper, we explore a class of distributional instance segmentation models using latent codes that can model uncertainty over plausible hypotheses of object masks. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary in industrial use cases. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes in a robotic application. On a real-world apparel-picking robot, our method significantly reduces double pick errors while maintaining high performance.
Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies is a critical requirement to enable the real-world deployment of AI agents. Recently, Signal Temporal Logic (STL) has been shown to be an effective language for encoding rules as spatio-temporal constraints. This work uses Monte Carlo Tree Search (MCTS) as a means of integrating STL specification into a vanilla LfD policy to improve constraint satisfaction. We propose augmenting the MCTS heuristic with STL robustness values to bias the tree search towards branches with higher constraint satisfaction. While the domain-independent method can be applied to integrate STL rules online into any pre-trained LfD algorithm, we choose goal-conditioned Generative Adversarial Imitation Learning as the offline LfD policy. We apply the proposed method to the domain of planning trajectories for General Aviation aircraft around a non-towered airfield. Results using the simulator trained on real-world data showcase 60% improved performance over baseline LfD methods that do not use STL heuristics. [Code] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Codebase: https://github.com/castacks/mcts-stl-planning [Video] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Video: https://youtu.be/fiFCwc57MQs
Self-adaptive Teaching-learning-based Optimizer with Improved RBF and Sparse Autoencoder for Complex Optimization Problems

Evolutionary algorithms are commonly used to solve many complex optimization problems in such fields as robotics, industrial automation, and complex system design. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-learning-based Optimizer is designed to dynamically adjust parameters for balancing exploration and exploitation during its solution process. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate it by comparing it with several state-of-the-art algorithms through six benchmark functions. We further test it by applying it to solve a real-world computational offloading problem.
Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network

This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network. Our method does not require any thermal annotations or co-registered RGB-thermal pairs, enabling robots to perform visual tasks at night and in adverse weather conditions without incurring additional costs of data labeling and registration. Current unsupervised domain adaptation methods look to align global images or features across domains. However, when the domain shift is significantly larger for cross-modal data, not all features can be transferred. We solve this problem by using a shared backbone network that promotes generalization, and domain-specific attention that reduces negative transfer by attending to domain-invariant and easily-transferable features. Our approach outperforms the state-of-the-art RGB-to-thermal adaptation method in classification benchmarks, and is successfully applied to thermal river scene segmentation using only synthetic RGB images. Our code is made publicly available at https://github.com/ganlumomo/thermal-uda-attention.
MVTrans: Multi-View Perception of Transparent Objects

Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings. Existing methods utilize RGB-D or stereo inputs to handle a subset of perception tasks including depth and pose estimation. However transparent object perception remains to be an open problem. In this paper, we forgo the unreliable depth map from RGB-D sensors and extend the stereo based method. Our proposed method, MVTrans, is an end-to-end multi-view architecture with multiple perception capabilities, including depth estimation, segmentation, and pose estimation. Additionally, we establish a novel procedural photo-realistic dataset generation pipeline and create a large-scale transparent object detection dataset, Syn-TODD, which is suitable for training networks with all three modalities, RGB-D, stereo and multi-view RGB. https://ac-rad.github.io/MVTrans/
Real World Offline Reinforcement Learning with Realistic Data Source

Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience. However, current ORL benchmarks are almost entirely in simulation and utilize contrived datasets like replay buffers of online RL agents or sub-optimal trajectories, and thus hold limited relevance for real-world robotics. In this work (Real-ORL), we posit that data collected from safe operations of closely related tasks are more practical data sources for real-world robot learning. Under these settings, we perform an extensive (6500+ trajectories collected over 800+ robot hours and 270+ human labor hour) empirical study evaluating generalization and transfer capabilities of representative ORL methods on four real-world tabletop manipulation tasks. Our study finds that ORL and imitation learning prefer different action spaces, and that ORL algorithms can generalize from leveraging offline heterogeneous data sources and outperform imitation learning. We release our dataset and implementations at URL: https://sites.google.com/view/real-orl.
StereoVAE: A lightweight stereo-matching system using embedded GPUs

We propose a lightweight system for stereo-matching using embedded graphic processing units (GPUs). The proposed system overcomes the trade-off between accuracy and processing speed in stereo matching, thus further improving the matching accuracy while ensuring real-time processing. The basic idea is to construct a tiny neural network based on a variational autoencoder (VAE) to achieve the upscaling and refinement a small size of coarse disparity map. This map is initially generated using a traditional matching method. The proposed hybrid structure maintains the advantage of low computational complexity found in traditional methods. Additionally, it achieves matching accuracy with the help of a neural network. Extensive experiments on the KITTI 2015 benchmark dataset demonstrate that our tiny system exhibits high robustness in improving the accuracy of coarse disparity maps generated by different algorithms, while running in real-time on embedded GPUs.
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach

Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow con-vergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is not satisfactory. In this paper, we propose a Consensus-based Sim-And-Real deep reinforcement learning algorithm (CSAR) for manipulator pick-and-place tasks, which shows comparable performance in both sim-and- real worlds. In this algorithm, we train the agents in simulators and the real world to get the optimal policies for both sim-and-real worlds. We found two interesting phenomenons: (1) Best policy in simulation is not the best for sim-and-real training. (2) The more simulation agents, the better sim-and-real training. The experimental video is available at: https://youtu.be/mcHJtNIsTEQ.
An Open Approach to Energy-Efficient Autonomous Mobile Robots

Autonomous mobile robots (AMRs) have the capability to execute a wide range of tasks with minimal human intervention. However, one of the major limitations of AMRs is their limited battery life, which often results in interruptions to their task execution and the need to reach the nearest charging station. Optimizing energy consumption in AMRs has become a critical challenge in their deployment. Through empirical studies on real AMRs, we have identified a lack of coordination between computation and control as a major source of energy inefficiency. In this paper, we propose a comprehensive energy prediction model that provides real-time energy consumption for each component of the AMR. Additionally, we propose three path models to address the obstacle avoidance problem for AMRs. To evaluate the performance of our energy prediction and path models, we have developed a customized AMR called Donkey, which has the capability for fine-grained (millisecond-level) end-to-end power profiling. Our energy prediction model demonstrated an accuracy of over 90% in our evaluations. Finally, we applied our energy prediction model to obstacle avoidance and guided energy-efficient path selection, resulting in up to a 44.8% reduction in energy consumption compared to the baseline.
Credible Online Dynamics Learning for Hybrid UAVs

Hybrid unmanned aerial vehicles (H-UAVs) are highly versatile platforms with the ability to transition between rotary- and fixed-wing flight. However, their (aero)dynamics tend to be highly nonlinear which increases the risk of introducing safety-critical modeling errors in a controller. Designing a safe, yet not too cautious controller, requires a credible model which provides accurate dynamics uncertainty quantification. We present a data-efficient, probabilistic semi-parametric dynamics modeling approach that allows for online, filter-based inference. The proposed model leverages prior knowledge using a nominal parametric model, and combines it with residuals in the form of sparse Gaussian processes to account for possibly unmodeled forces and moments. Uncertain nominal and residual parameters are jointly estimated using Bayesian filtering. The resulting model accuracy and the reliability of its predicted uncertainty are analyzed for both a simulated and a real example, where we learn the 6DoF nonlinear dynamics of a tiltwing H-UAV from a few minutes of flight data. Compared to a residual-free nominal model, the proposed semi-parametric approach provides increased model accuracy in relevant parts of the flight envelope and substantially higher credibility overall.
Monocular Visual-Inertial Depth Estimation

We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visual- inertial odometry to produce dense depth estimates with metric scale. Our approach performs global scale and shift alignment against sparse metric depth, followed by learning-based dense alignment. We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in inverse RMSE with dense scale alignment relative to performing just global alignment alone. Our approach is especially competitive at low density; with just 150 sparse metric depth points, our dense- to-dense depth alignment method achieves over 50 % lower iRMSE over sparse-to-dense depth completion by KBNet, currently the state of the art on VOID. We demonstrate successful zero-shot transfer from synthetic TartanAir to real-world VOID data and perform generalization tests on NYUv2 and VCU-RVI. Our approach is modular and is compatible with a variety of monocular depth estimation models.
EWareNet: Emotion-Aware Pedestrian Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation

We present EWareNet, a novel intent and affect-aware social robot navigation algorithm among pedestrians. Our approach predicts the trajectory-based pedestrian intent from gait sequence, which is then used for intent-guided navigation taking into account social and proxemic constraints. We propose a transformer-based model that works on commodity RGB-D cameras mounted onto a moving robot. Our intent prediction routine is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. Our navigation scheme consists of a novel obstacle profile representation methodology that is dynamically adjusted based on the pedestrian pose, intent, and affect. The navigation scheme is based on a reinforcement learning algorithm that takes pedestrian intent and robot's impact on pedestrian intent into consideration, in addition to the environmental configuration. We outperform current state-of-art algorithms for intent prediction from 3D gaits.
Sampling-based path planning under temporal logic constraints with real-time adaptation

Replanning in temporal logic tasks is extremely difficult during the online execution of robots. This study introduces an effective path planner that computes solutions for temporal logic goals and instantly adapts to non-static and partially unknown environments. Given prior knowledge and a task specification, the planner first identifies an initial feasible solution by growing a sampling-based search tree. While carrying out the computed plan, the robot maintains a solution library to continuously enhance the unfinished part of the plan and store backup plans. The planner updates existing plans when meeting unexpected obstacles or recognizing flaws in prior knowledge. Upon a high-level path is obtained, a trajectory generator tracks the path by dividing it into segments of motion primitives. Our planner is integrated into an autonomous mobile robot system, further deployed on a multicopter with limited onboard processing power. In simulation and real-world experiments, our planner is demonstrated to swiftly and effectively adjust to environmental uncertainties.
Handwritten Digit Recognition using Deep Neural Networks

People have never been extra reliant on machines than they're nowadays. As an instance, neural network and machine learning algorithms may be used to do everything to classify objects in films for adding some sound to silent films. Similarly, one of the many regions of studies and development is handwritten textual content reputation, which has a plethora of ability programs. Handwritten Recognition (HWR), many times also called as Transcribed textual content acknowledgment Handwritten Text Recognition (HTR), is the capability of a system to benefit and decipher comprehensible manually written input collected from various sources. On this paper, here used Convolution Neural network (CNN), support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) models to perform handwritten digit reputation on MNIST datasets. The main objective is to determine the quality possible model for digit recognition with the aid of evaluating the accuracy and execution instances of the aforementioned models. The CNN model was able to give 99.53% accuracy on testing data.
Implementation of Machine Learning Classification Techniques for Intrusion Detection System

In the region internet including service, flaws of software design that is present on the device on its own, protocol, and also in control. These vulnerabilities of the community can be very easily hacked by the community hackers and to the net put the potential intrusion attacks. With the time as there is an increase in cases of personal computers security attacks and also increase in the number, fundamental security attacks are most in the number of cases than before. No long these issues of community safety can be ignored. The protection of these types of assets is entailed by computer protection. Information's Availability, integrity, and confidentiality to the users are legal characteristics this may be represented. Properties of privacy and secrecy are deal with confidentiality. On the negative chose set of rules finds attacks and intrusion. This machine of intrusion detection is a machine sturdily gaining knowledge of capability and excellent adaptive capacity. Using it may stumble on numerous unknown kinds of intrusions of attacks and intrusions.
Deadline and Cost Optimization based Task Scheduling (DCOTS) in Cloud Computing Environment

Deadline and cost optimization are the important issues in cloud computing environment. Lack of these two factors discourages the cloud users from using the services of clouds. The proposed task scheduling considers fulfilling user's requirements with in time and budget constraints. Cloud users always try to maximize their profit and finish the task or job within a predetermined time. Thus, proposed Deadline and Cost Optimization based Task Scheduling (DCOTS) guarantees to satisfy user's need and try to maximize the profit of cloud providers. Proposed approach measures the QoS parameters i.e make span, load balancing, response time, throughput etc of cloud resources and these parameters are also associated with deadline and cost. The effectiveness of proposed scheduling algorithm is compared with existing cost and time based scheduling algorithm.
Research on Deblurring Method for Insulator Images Based on Channel and Spatial Attention Mechanisms

The motion blur problem of aerial insulator images seriously affects the accuracy of insulator defect detection and recognition. Traditional convolutional neural networks treat channel and spatial information equally when processing insulator images, resulting in the network being unable to fully consider the relationship between feature channels, and allocating weights unreasonably and losing details during the image processing. Therefore, an insulator image deblurring method based on channel and spatial attention mechanism is proposed in this paper. The channel attention mechanism can select clearer parts of the image based on attention weights for different channels, while the spatial attention mechanism can better capture the detailed information in insulator images based on attention weights for different regions. This paper combines L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> loss and edge loss as the comprehensive loss function to improve the visual quality and edge sharpness of the restored image. Through experimental analysis, it can be seen from the results that the image defuzzification method proposed in this paper has significantly improved on the index of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM).
Effects of Quantum computing on Businesses

This study examines the effect of quantum computing on businesses. The study begins by providing an overview of quantum computing and its main features. Also discusses the benefits and challenges of quantum computing for businesses, including improved performance, increased efficiency, and enhanced security. This study examines how quantum computing will affect several sectors, including banking, healthcare, and logistics. The study's findings suggest that quantum computing has the ability to fundamentally alter company operations and open up new avenues for innovation. This also highlights the need for businesses to invest in developing their quantum computing capabilities, as well as to consider the potential risks and challenges associated with this technology. The study concludes by telling the importance of staying ahead and investing in quantum computing and taking advantage of its potential benefits for businesses.