Learning of Tool Force Adjustment Skills by a Life-sized Humanoid using Deep Reinforcement Learning and Active Teaching Request
Yoichiro Kawamura,Masaki Murooka,Naoki Hiraoka,Hideaki Ito,Kei Okada,Masayuki Inaba,Yoichiro Kawamura,Masaki Murooka,Naoki Hiraoka,Hideaki Ito,Kei Okada,Masayuki Inaba
The purpose of this study is to make life-sized humanoid robots acquire tool manipulation skills that require complicated force adjustment. The difficulty in acquisition of tool manipulation skills comes from the hardship in physical modeling. Recent research have revealed that deep reinforcement learning (DRL), a model-free approach, performs superior in such tasks. However, DRL in general has a ...
