首页|A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object

A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object

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This paper presents a learning-based control framework for fast(＜1.5 s)and accurate manipulation of a flexible object,i.e.,whip targeting.The framework consists of a motion planner learned or optimized by an algorithm,Online Impedance Adaptation Control(OIAC),a sim2real mechanism,and a visual feedback component.The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning(DRL),a nonlinear optimization,and a genetic algorithm in learning generalization of motion planning.It can greatly reduce average learning trials(to＜20％of others)and maximize average rewards(to＞3 times of others).Besides,motion tracking errors are greatly reduced to 13.29％and 22.36％of constant impedance control by the OIAC of the proposed framework.In addition,the trajectory similarity between simulated and physical whips is 89.09％.The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a flexible object.

Deep reinforcement learningDeformable object manipulationVariable impedance controlSim2realVisual tracking

Junyi Wang、Xiaofeng Xiong、Silvia Tolu、Stanislav N.Gorb

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Department of Electrical and Photonics Engineering,Technical University of Denmark,Anker Engelunds Vej 101,Kongens Lyngby,Zealand 2800,Denmark

SDU Biorobotics,Maersk Mc-Kinney Møller Institute,University of Southern Denmark(SDU),Campusvej 55,Odense M,Funen 5230,Denmark

Department Functional Morphology and Biomechanics,Zoological Institute,University of Kiel,Am Botanischen Garten 1-9,D-24118 Kiel,Schleswig-Holstein,Germany

Br?drene HartmannsThomas B.ThrigesFabrikant Mads ClausensEnergiFyn fundsFunding Open access funding provided by University of Southern Denmark

A367757648-21062023-0210

2024

10.1007/s42235-024-00534-2

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(4)
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