Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
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Reinforcement Learning of Shared Control Policies for Dexterous Telemanipulation
-Application to a Page Turning Task
Takahiro HasegawaTakamitsu MatsubaraKenji Sugimoto
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JOURNAL FREE ACCESS

2016 Volume 29 Issue 8 Pages 346-354

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Abstract

Recently, the framework of shared control between a human operator and a robot system using a cooperative controller with human operator to decide control input to a robot system has been drawing much attention for dexterous telemanipulation. However, desiging a suitable controller for shared control scheme for the given task is difficult due to the difficulties in modeling of operator'’s behavior and environment. In this paper,we proposed a model-free approach using reinforcement learning to learn a shared control policy through interactions with the operator, robot and environment. To validate our method,we adopted a page turning task by telemanipulation and developed an experimental platform with a physical simulator.Experimental results suggest that our method is able to learn task-relevant shared control for flexible and enhanced dexterous manipulation.

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© 2016 The Institute of Systems, Control and Information Engineers
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