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检索条件"任意字段=26th International Conference on Methods and Models in Automation and Robotics"

共 1175 条记录，以下是11-20 订阅

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2011 16th international conference on methods and models in automation and robotics, MMAR 2011

2011 16th International Conference on Methods and Models in ...

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2011 16th international conference on methods and models in automation and robotics, MMAR 2011

ISBN: (纸本)9781457709128

the proceedings contain 77 papers. the topics discussed include: frequency response identification in the case of periodic disturbances;experimental parameter identification for a control-oriented model of the thermal behavior of high-temperature fuel cells;a methodology of estimating hybrid black-box -prior knowledge models of an industrial processes;an optimal sequence of tasks for autonomous learning systems;parallel computation approach to solve total cost minimization scheduling problem;local search metaheuristics for some discrete-continuous project scheduling problems with discounted cash flows;sensitivity analysis of parabolic-hyperbolic optimal control problems;local constrained controllability of second order dynamical systems with delay;spreadability, vulnerability, regional viability and protector control;and continuation method approach to trajectory planning in robotic systems.

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14th international conference on methods and models in automation and robotics, MMAR'09 - Proceedings

14th International Conference on Methods and Models in Autom...

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14th international conference on methods and models in automation and robotics, MMAR'09

ISBN: (纸本)9783902661555

the proceedings contain 122 papers. the topics discussed include: observer-based control of a continuous stirred tank reactor with recycle time-delay;sensitivity analysis of time delay hyperbolic optimal control problems;constrained controllability of second order dynamical systems with delay;optimization problems of parabolic-hyperbolic systems with time delays;optimal control of a time delay infinite order hyperbolic system with pointwise observation of the state;configuration of sensor network with uncertain location of nodes for parameter estimation in distributed parameter systems;numerical experiments in drag minimization for compressible Navier-Stokes flows in bounded domains;sensitivity analysis of infinite order parabolic optimal control problems;neural adapted controller learned on-line in real-time;genetic algorithm based optimization for EMG pattern recognition system;and a fault-tolerant control scheme with input constraints for Takagi-Sugeno fuzzy systems.

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Utilizing Large Language models for Robot Skill Reward Shaping in Reinforcement Learning 17th

Utilizing Large Language Models for Robot Skill Reward Shapi...

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17th international conference on Intelligent robotics and Applications

作者： Guo, Qi Liu, Xing Hui, Jianjiang Liu, Zhengxiong Huang, Panfeng Northwestern Polytech Univ Res Ctr Intelligent Robot Sch Astronaut Xian 710072 Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

In this paper, we examines the integration of LLMs in designing reward functions for reinforcement learning (RL) to enhance robotic applications with minimal human input. In RL, the reward function is pivotal, guiding the agent's learning trajectory by evaluating the desirability of behaviors within specific environments. Traditional reward functions, often sparse, lead to slow convergence as agents require extensive interactions to learn effectively. By leveraging LLM's ability to generate code from task semantics, we propose a new method that reduces the complexity of reward design, allowing even non-experts to create effective reward policies using semantic prompts. We utilize the Soft ActorCritic (SAC) algorithm, known for its efficiency and stability, to train agents under these conditions. To validate the efficacy of our method, we compare it with traditional techniques like Trajectory-ranked reward extrapolation (T-REX). Our findings indicate that the LLM-generated rewards enable quicker convergence and are as effective as those crafted through conventional methods, demonstrating the potential of LLMs to revolutionize reward shaping in RL. Furthermore, we transferred the robot door-opening task from the real-world simulation environment to a real robot, achieving *** approach allows for the rapid deployment of robotic systems, making sophisticated robotics technology more accessible and feasible for a wider range of applications. this study underscores the transformative impact of integrating advanced language models into the realm of robotics and RL, opening up new avenues for future research and application.

关键词： Large language model(LLMs) reward shaping reinforcement learning

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Few-Shot N-Ary Knowledge Inference Using Large Language models 1

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17th international conference on Intelligent robotics and Applications

作者： Lang, Fang Song, Wei Zhu, Qiuguo Zhu, Shiqiang Zhejiang Univ Hangzhou Peoples R China Zhejiang Univ Inst Robot Ningbo Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

Predicting object locations in everyday scenarios is critical for service robots performing tasks such as object search and planning. Previous knowledge reasoning methods mostly use object class for prediction while neglecting n-ary multimodal properties. In this paper, we introduce a novel Few-shot n-Ary Knowledge Inference framework, FAKI, aimed to infer target locations using large language models (LLMs). By integrating few-shot In-Context Learning (ICL) method, we select the top-k most similar samples using K-Nearest Neighbors (KNN) for contextual information. To solve incomplete observations in few-shot scenarios, we provide the Metainfo, which contains data distribution in the trainset as hints for LLM. Leveraging KNN and Metainfo, our method achieves state-of-the-art (SOTA) performance on the LINK dataset for location prediction, outperforming the previous SOTA by 17.6%. Furthermore, we investigate the effects of prompt design, ICL, and top-k selection on prediction accuracy, providing deeper insights in few-shot n-ary knowledge inference.

关键词： Large Language Model In-Context Learning N-ary Knowledge Inference

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Machine Learning models for Gait Phases Detection Using Surface Electromyography Signals 17th

Machine Learning Models for Gait Phases Detection Using Surf...

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17th international conference on Intelligent robotics and Applications

作者： Feng, Zhiyang Jiang, Zhengxuan Liu, Huayue Wang, Wenkong Wang, Yiqi Lu, Chang Ma, Xin Li, Yibin Song, Rui Zhang, Huanghe Shandong Univ Sch Control Sci & Engn Ctr Robot Jinan Shandong Peoples R China Univ Warwick Coventry W Midlands England Heidelberg Univ Fac Med Heidelberg Germany Heidelberg Univ Hosp Inst Computat Biomed Heidelberg Germany

ISBN: (纸本)9789819607853;9789819607860

Accurate gait phase detection is a fundamental technology for wearable rehabilitation robots, enabling them to deliver effective, safe, and personalized assistance that closely aligns with the therapeutic needs and safety requirements of users. Unlike force sensors or inertial measurement units, surface electromyography (sEMG) signals establish a direct link to the wearer's motion intentions, making them ideally suited for real-time applications. this study investigates the utilization of sEMG signals to identify gait phases using three types of machine learning models: k-nearest neighbors (KNN), support vector machines (SVM), and artificial neural networks (ANN). the models are trained on a publicly available dataset (i.e., SIAT-LLMD), which consists of data from 40 healthy individuals and utilizes three training methods: holdout, 10-fold cross-validation, and leave-one-subject-out cross-validation. ANN achieved the best results, demonstrating an overall accuracy of 92.6%+/- 2.6% for detecting stance and swing phases, and 94.4%+/- 4.2% for detecting five gait sub-phases (i.e., stance flexion phase, stance extension phase, preswing phase, swing flexion phase, and swing extension phase).

关键词： EMG signals Gait phase detection Machine learning Artificial neural networks

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Human-Robot Pose Tracking Based on CNN with Color and Geometry Aggregation 16th

Human-Robot Pose Tracking Based on CNN with Color and Geo...

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16th international conference on Social robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. the variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. the iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. the DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. this algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. the algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Integrating Retrospective Framework in Multi-Robot Collaboration

Integrating Retrospective Framework in Multi-Robot Collabora...

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international conference on Autonomous Robots and Agents, ICARA

作者： Jiazhao Liang Hao Huang Yu Hao Geeta Chandra Raju Bethala Congcong Wen Yi Fang NYUAD Center for Artificial Intelligence and Robotics (CAIR) Abu Dhabi UAE Embodied AI and Robotics (AIR) Lab NYU Abu Dhabi UAE New York University Abu Dhabi UAE New York University US

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Recent advancements in Large Language models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing methods often struggle to achieve efficient collaboration and decision-making in dynamic and uncertain environments, which are common in real-world multi-robot scenarios. To address these challenges, we propose a novel retrospective actor-critic framework for multi-robot collaboration. this framework integrates two key components: (1) an actor that performs real-time decision-making based on observations and task directives, and (2) a critic that retrospectively evaluates the outcomes to provide feedback for continuous refinement, such that the proposed framework can adapt effectively to dynamic conditions. Extensive experiments conducted in simulated environments validate the effectiveness of our approach, demonstrating significant improvements in task performance and adaptability. this work offers a robust solution to persistent challenges in robotic collaboration.

关键词： Robot kinematics Large language models Decision making Collaboration Robustness Real-time systems Planning Multi-robot systems Resource management Tuning

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829598

the proceedings contain 104 papers. the special focus in this conference is on Computer Aided Systems theory. the topics include: Influence of Spike Encoding, Neuron models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production Systems;Using automationML for Advanced Simulation in Industrial automation;AR Digital Twin Demonstrator for Industrial robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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Field Oriented Control of BLDC Motors for Unmanned Underwater Vehicle (UUV)

Field Oriented Control of BLDC Motors for Unmanned Underwate...

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international conference on Autonomous Robots and Agents, ICARA

作者： Basavaraj S Talikoti Devindra M Chitguppa Rahul Kumar Mohan Karnik Automation Solutions PDIC Bharat Electronics Limited Bengaluru India

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

this paper delves into the domain of Brushless Direct Current (BLDC) Motors that have become significantly popular in modern day applications like automotive, aerospace, robotics (unmanned vehicles) etc. because of the edge they have over their brushed counterpart. Unlike brushed DC motors, which use brush and commutator segments for commutation, BLDC motors use electronic commutation which eliminates wear and tear of mechanical parts, frictional loss etc. this paper discusses mathematical modelling of a BLDC motor, presents the insights about design and implementation of Field Oriented Control (FOC), initial calculation of Proportional Integral Differential (PID) gains and final implementation of FOC algorithm for BLDC Motors of UUV. this technique is far superior than other traditional methods, like Trapezoidal control and sinusoidal control, and implements independent control of torque and flux with the help of various transforms. It is a very useful and precise technique which results in a superior dynamic response, less torque ripple and high efficiency. the key objective is to dispense a holistic approach for designing PID controller for a BLDC motor. the performance of the controller was tested for no load and load condition. the final results demonstrated the anticipated superior dynamic response.

关键词： Dynamic response Motor drives Accuracy Heuristic algorithms Commutation Motors Real-time systems Mathematical models Tuning Robots

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829512

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