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Assist system for remote manipulation of electric drills by the robot “WAREC-1R” using deep reinforcement learning

Published online by Cambridge University Press:  04 June 2021

Xiao Sun Open the ORCID record for Xiao Sun [Opens in a new window] ,
Hiroshi Naito ,
Akio Namiki ,
Yang Liu ,
Takashi Matsuzawa  and
Atsuo Takanishi
Xiao Sun*
Affiliation:
Department of Mechatronics, University of Yamanashi, Yamanashi,Japan,
Hiroshi Naito
Affiliation:
Department of Modern Mechanical Engineering, Waseda University, Tokyo, Japan
Akio Namiki
Affiliation:
Department of Mechanical Engineering, Chiba University, Chiba, Japan
Yang Liu
Affiliation:
Department of Mechanical Engineering, Chiba University, Chiba, Japan
Takashi Matsuzawa
Affiliation:
Department of Modern Mechanical Engineering, Waseda University, Tokyo, Japan
Atsuo Takanishi
Affiliation:
Department of Modern Mechanical Engineering, Waseda University, Tokyo, Japan
*
*Corresponding author. Email: xsun@yamanashi.ac.jp
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Abstract

Operation of tools has long been studied in robotics. Although appropriate hold of the tool by robots is the base of successful tool operation, it is not with ease especially for tools with complicated shape. In this paper, an assist system for a four-limbed robot is proposed for remote operation of reaching and grasping electric drills using deep reinforcement learning. Through comparative evaluation experiments, the increase of success rate for reaching and grasping is verified and the decrease in both physical and mental workload of the operator is also validated by the index of NASA-TLX.

Keywords

ManipulationDeep reinforcement learningRemote operation

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Type
Article
Information
Robotica , Volume 40 , Issue 2 , February 2022 , pp. 365 - 376
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

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