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检索条件"主题词=Robotics and automation"

共 11731 条记录，以下是91-100 订阅

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Distributional Reinforcement Learning with Sample-set Bellman Update

Distributional Reinforcement Learning with Sample-set Bellma...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Weijian Zhang Jianshu Wang Yang Yu Polixir.ai Nanjing China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University and Polixir.ai Nanjing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Distributional Reinforcement Learning (DRL) not only endeavors to optimize expected returns, but also strives to accurately characterize the full distribution of these returns, a key aspect in enhancing risk-aware decision-making. Previous DRL implementations often inappropriately treat statistical estimations as concrete samples, which undermines the integrity of learning. While several studies have addressed this issue, they frequently give rise to new complications, including computational burdens and diminished stochastic behavior. In our work, we present a novel DRL framework that leverages the Gaussian mixture model to adeptly depict the distribution of returns. This approach ensures precise, authentic sampling critical for robust learning, while also preserving computational tractability. Through extensive evaluation on a diverse array of 59 Atari games, our method not only surpasses the efficacy of prior DRL algorithms but also presents formidable competition to contemporary top-tier RL algorithms, signifying a substantial advancement in the field.

关键词： Decision making Stochastic processes Estimation Reinforcement learning Games Concrete robotics and automation

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Machine Learning Based Energy Prediction of Hybrid Solar-Wind Energy Harvesters Using MPPT

Machine Learning Based Energy Prediction of Hybrid Solar-Win...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Muhammad Abdullah Khan Muhammad Taha Rehan Shahid Muhammad Subhan Asad Hassan Elahi Muhammad Abdullah Tayyab Department of Mechatronics Engg. CEME NUST Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

The ever-growing demand for reliable and sustainable energy solutions has shifted the trend from standalone power systems towards higher efficient Hybrid Solar-Wind Power Systems. This paper presents a comparative analysis of a machine learning based energy prediction for a hybrid solar-wind energy harvester using MPPT. The solar-wind hybrid systems interfaced with MPPT can simultaneously capture both solar and wind power, and machine learning algorithms are involved for optimized multi-mode operation across different environmental conditions. The models used including Support Vector Machine (SVM), Random Forest Regression (RF), K-Nearest Neighbors (KNN), Linear Regression (LR), Decision Tree (DT), and Linear Discriminant Analysis (LDA) to analyze the performance of hybrid energy harvester. This type of energy combination is beneficial for coastal areas and optimal solution for sustainable energy production from renewable sources.

关键词： Support vector machines Renewable energy sources Sea measurements Nearest neighbor methods Wind power generation Hybrid power systems Reliability robotics and automation Regression tree analysis Random forests

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Enhanced PID Neural Network Control (EPIDNNC) - A Model Reference Approach

Enhanced PID Neural Network Control (EPIDNNC) - A Model Refe...

引用

International Conference on robotics and automation Engineering (ICRAE)

作者： Dalim Wahby Guillaume Ducard Université Côte d’Azur I3S/CNRS Sophia Antipolis France

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

PID controllers are among the most popular control strategies used in industry. However, one of their main limitations is often the difficulty to tune them properly to achieve the best possible performance. Therefore, adaptive PID methods have been developed, such as PID neural networks (PIDNN). The gains are often tuned by minimizing the error between the desired output and the system’s output, using a gradient descent-based algorithm. We propose an enhanced PIDNN controller (EPIDNNC) that introduces a reference model, to teach the controller a desired response. This work is successfully applied to a nonlinear system, an inverted pendulum, whose dynamics and parameters are unknown to the controller. Our EPIDNNC method is superior to the standard PIDNNC, due to 1) better performance, with respect to overshoot, steady-state error, settling time, and oscillatory behavior, and 2) a new versatile update law, allowing changes in the reference model dynamics.

关键词： Industries Adaptation models Adaptive systems Heuristic algorithms Neural networks Cost function Steady-state Nonlinear dynamical systems robotics and automation Standards

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Multi-Level Action Tree Rollout (MLAT-R): Efficient and Accurate Online Multiagent Policy Improvement

Multi-Level Action Tree Rollout (MLAT-R): Efficient and Accu...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Andrea Henshall Sertac Karaman Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA Faculty of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Rollout algorithms are renowned for their abilities to correct for the suboptimalities of offline-trained base policies. In the multiagent setting, performing online rollout can require an exponentially large number of optimizations with respect to the number of agents. One-agent-at-a-time algorithms offer computationally efficient approaches to guaranteed policy improvement; however, this improvement is with respect to a state value estimate derived from a potentially poor base policy. Monte Carlo tree search (MCTS) provably converges to the true state value estimates; however, the exponentially large search space often makes its online use limited. Here, we present the Multi-Level Action Tree Rollout (MLAT-R) algorithm. MLAT-R provides 1) provable improvement over a base policy, 2) policy improvement with respect to the true state value, 3) applicability to any number of agents, and 4) an action space that grows linearly with the number of agents rather than exponentially. In this paper, we outline the algorithm, sketch a proof of its improvement over a base policy, and evaluate its performance on a challenging problem for which the base policy cannot reach a terminal state. Despite the challenging experimental setup, our algorithm reached a terminal state in 86% of all experiments, compared to 31% for state-of-the-art one-agent-at-a-time algorithms. In experiments involving MCTS, MLAT-R reached a terminal state in 99% of experiments compared to 92% for MCTS. MLAT-R achieved these results while considering an exponentially smaller action space than MCTS.

关键词： Monte Carlo methods Accuracy Computational efficiency robotics and automation Optimization

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Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adapt...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Haoxiang Ma Ran Qin Modi Shi Boyang Gao Di Huang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We then propose a global-to-local alignment pipeline with individual global domain classifiers for scene features of RGB and depth images as well as a local one specifically working for grasp features in the two modalities. In particular, we propose a grasp prototype adaptation module, which aims to facilitate fine-grained local feature alignment by dynamically updating and matching the grasp prototypes from the simulation and real-world scenarios throughout the training process. Due to such designs, the proposed method substantially reduces the domain shift and thus leads to consistent performance improvements. Extensive experiments are conducted on the GraspNet-Planar benchmark and physical environment, and superior results are achieved which demonstrate the effectiveness of our method. Code is available at https://***/mahaoxiang822/GL-MSDA.

关键词： Training Codes Pipelines Prototypes Benchmark testing Robustness robotics and automation

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Weighting Online Decision Transformer with Episodic Memory for Offline-to-Online Reinforcement Learning

Weighting Online Decision Transformer with Episodic Memory f...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Xiao Ma Wu-Jun Li Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University Nanjing PRC

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Offline reinforcement learning (RL) has been shown to be successfully modeled as a sequence modeling problem, drawing inspiration from the success of Transformers. Offline RL is often limited by the quality of the offline dataset, so offline-to-online RL is a more realistic setting. Online decision transformer (ODT) is an effective and representative sequence modeling-based offline-to-online RL method. Despite its effectiveness, ODT still suffers from the sample inefficiency problem during the online fine-tuning phase. This sample inefficiency problem arises because the agent treats all state-action pairs in the replay buffer equally when trying to learn from the replay buffer. In this paper, we propose a simple yet effective method, called weighting online decision transformer with episodic memory (WODTEM), to improve sample efficiency. We first attempt to introduce an episodic memory (EM) mechanism into the sequence modeling-based RL methods. By utilizing the EM mechanism, we propose a novel training objective with a weighting function, based on ODT, to improve sample efficiency. Experimental results on multiple tasks show that WODTEM can improve sample efficiency.

关键词： Training Memory management Reinforcement learning Transformers Task analysis robotics and automation

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A Trajectory-based Flight Assistive System for Novice Pilots in Drone Racing Scenario

A Trajectory-based Flight Assistive System for Novice Pilots...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Yuhang Zhong Guangyu Zhao Qianhao Wang Guangtong Xu Chao Xu Fei Gao College of Control Science and Engineering Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Drone racing has become a popular international competition and has attained wide attention in recent years. However, the requirements of high-level operation keep the novice pilots away from participating in it. This paper presents a trajectory-based flight assistive system that enables various operators to fly the drone in a racing scene at a high speed. The whole system is structured hierarchically, consisting of both offline and online components. In the offline part, a global time-optimal trajectory is generated as the expert reference, and a dense flight corridor is constructed to provide sufficiently large safe region. In the online part, a remote control-mapped primitive is designed to fast encapsulate pilots’ inputs, and the time mapping based trajectory progress is customized to further capture intention. Then, a trajectory planner is proposed to generate intention-aligned, smooth, feasible, and safe trajectories periodically. Additionally, a yaw planning that provides the pilot with the best suitable view angle is employed to further alleviate the operation difficulty. Simulations and real world experiments are implemented to verify the performance of our system. The maximum flight speed can reach 6.0 m/s for a novice drone pilot in a real racing scene. Our code is released as an open-source package 1 .

关键词： Codes Trajectory Planning robotics and automation Drones

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Human-Exoskeleton Locomotion Interaction Experience Transfer: Speeding up and Improving the Performance of Preference-based Optimizations of Exoskeleton Assistance During Walking

Human-Exoskeleton Locomotion Interaction Experience Transfer...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Hongwu Li Junchen Liu Ziqi Wang Haotian Ju Tianjiao Zheng Yongsheng Gao Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we propose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET framework could speed up the exploration process and acquire more satisfying optimized results for naive wearers.

关键词： Legged locomotion Exoskeletons Process control Optimization methods robotics and automation

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Soft Hand Extension Glove with Thumb Abduction and Extension Assistance

Soft Hand Extension Glove with Thumb Abduction and Extension...

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IEEE International Conference on robotics and automation (ICRA)

作者： Disheng Xie Yujie Su Xiangqian Shi Zheng Li Raymond Kai-yu Tong Department of Biomedical Engineering the Chinese University of Hong Kong Hong Kong SAR China Department of Surgery the Chinese University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Hand extension is crucial for stroke survivors with spasticity, where their fingers become rigid and their thumb remains curled within the palm. Due to the underactuated nature of the hand, the dominance of flexor muscles over extensors, and the limited surface area available, developing an extension glove with thumb assistance poses a challenge for researchers. This paper introduces a fully wearable soft hand extension glove based on the X-pouch and strap system, addressing the above challenges. The glove enables adequate finger extension, thumb abduction, and extension for high MAS score patients. Modelling and testing revealed extension torques of up to 2.7 Nm at the MCP joint and 0.67 Nm at the PIP and DIP joints. Performance evaluation, including comparison with existing methods, demonstrated the glove’s superior extension capabilities using a model hand with realistic stiffness. Furthermore, the glove’s effectiveness was confirmed through testing on a stroke patient with MAS = 2, validating its on-body functionality.

关键词： Performance evaluation Thumb Muscles robotics and automation Testing Electronics packaging

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A Simulation-based Optimization Method for the Number of Toll Lanes of New Toll Stations After Canceling Provincial Toll Station in China

A Simulation-based Optimization Method for the Number of Tol...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Xinfeng Xue Wei Peng Hunan Provincial Communications Planning Survey & Design Institute Co. Ltd. Changsha China Hunan Communications Research Institute Co. Ltd. Changsha China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

This paper focuses on the simulation-based optimization method for the number of toll lanes of new toll stations. Firstly, this paper analyses traffic characteristics of toll station in peak hour. The road users are divided into four categories: ETC (Electronic Toll Collection) passenger cars (with ETC equipment), ETC freight cars, MTC (Manual Toll Collection) passenger cars (without ETC equipment), and MTC freight cars. Then, a VISSIM based Vehicle Traffic and Service Time Simulation Model is calibrated. The input is the service time, and the calibration output is the traffic volume. After the well calibration of the traffic simulation model, the optimized number of ETC lanes and ETC/MTC mixed lanes at the toll station is determined respectively with increasing traffic volume. Finally, numerical results indicate that compared with the traditional method, the optimized number of toll lanes at the exit of the toll station are reduced significantly. It is further validated that this optimized number of toll lanes is also able to ease traffic congestion of the toll stations.

关键词： Solid modeling Roads Optimization methods Manuals Traffic control Calibration Numerical models Automobiles robotics and automation Traffic congestion

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