Reinforcement learning-based shared control for walking-aid robot and its experimental verification

Purpose This paper aims to propose a general framework of shared control for human–robot interaction. Design/methodology/approach Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof. Findings Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations. Originality/value Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.

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Experimental Verification of Snake-like Locomotion Using Model-based Reinforcement Learning

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2021.1p1-i10 ◽

2021 ◽

Vol 2021 (0) ◽

pp. 1P1-I10

Author(s):

Kotaro HIRAI ◽

Kyo KUTSUZAWA ◽

Dai OWAKI ◽

Mitsuhiro HAYASHIBE

Keyword(s):

Reinforcement Learning ◽

Experimental Verification ◽

Model Based

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Shared Control Design Methodologies of an Electric Wheelchair for Individuals with Severe Disabilities using Reinforcement Learning

Journal of Advanced Simulation in Science and Engineering ◽

10.15748/jasse.7.300 ◽

2020 ◽

Vol 7 (2) ◽

pp. 300-319

Author(s):

Lele Xi ◽

Motoki Shino

Keyword(s):

Reinforcement Learning ◽

Control Design ◽

Severe Disabilities ◽

Shared Control ◽

Electric Wheelchair ◽

Design Methodologies

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DEEP REINFORCEMENT LEARNING FOR HAPTIC SHARED CONTROL IN UNKNOWN TASKS

10.17771/pucrio.acad.50379 ◽

2020 ◽

Author(s):

FRANKLIN CARDENOSO FERNANDEZ

Keyword(s):

Reinforcement Learning ◽

Shared Control

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Reinforcement Learning of Shared Control Policies for Dexterous Telemanipulation

Transactions of the Institute of Systems Control and Information Engineers ◽

10.5687/iscie.29.346 ◽

2016 ◽

Vol 29 (8) ◽

pp. 346-354

Author(s):

Takahiro Hasegawa ◽

Takamitsu Matsubara ◽

Kenji Sugimoto

Keyword(s):

Reinforcement Learning ◽

Shared Control ◽

Control Policies

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Hierarchical Shared Control of Cane-Type Walking-Aid Robot

Journal of Healthcare Engineering ◽

10.1155/2017/8932938 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Chunjing Tao ◽

Qingyang Yan ◽

Yitong Li

Keyword(s):

Potential Field ◽

Control Method ◽

Sensory System ◽

Interaction Force ◽

Human Motion ◽

Shared Control ◽

Artificial Potential Field ◽

Intention Recognition ◽

Walking Aid ◽

Motion Intention

A hierarchical shared-control method of the walking-aid robot for both human motion intention recognition and the obstacle emergency-avoidance method based on artificial potential field (APF) is proposed in this paper. The human motion intention is obtained from the interaction force measurements of the sensory system composed of 4 force-sensing registers (FSR) and a torque sensor. Meanwhile, a laser-range finder (LRF) forward is applied to detect the obstacles and try to guide the operator based on the repulsion force calculated by artificial potential field. An obstacle emergency-avoidance method which comprises different control strategies is also assumed according to the different states of obstacles or emergency cases. To ensure the user’s safety, the hierarchical shared-control method combines the intention recognition method with the obstacle emergency-avoidance method based on the distance between the walking-aid robot and the obstacles. At last, experiments validate the effectiveness of the proposed hierarchical shared-control method.

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