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IEEE robotics and automation letters, v.5 no.4, 2020년, pp.5709 - 5716
Beltran-Hernandez, Cristian Camilo (Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan) , Petit, Damien (Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan) , Ramirez-Alpizar, Ixchel Georgina (Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan) , Nishi, Takayuki (Process Engineering & Technology Center, Research & Development Management Headquarters, FUJIFILM Corporation, Tokyo, Japan) , Kikuchi, Shinichi (Process Engineering & Technology Center, Research & Development Management Headquarters, FUJIFILM Corporation, Tokyo, Japan) , Matsubara, Takamitsu (Robot Learning Laboratory, Institute for Research Initiatives, Nara Institute of Science and Technology (NAIST), Ikoma, Japan) , Harada, Kensuke (Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan)
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Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators. ...
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