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Yoichiro Kawamura

3 accepted papers

2021

Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots

Kento Kawaharazuka, Koki Shinjo, Yoichiro Kawamura, Kei Okada, Masayuki Inaba
IROS 2021poster

In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be p…

Cited by 5Source ↗Scholar ↗
2021

Imitation Learning With Additional Constraints on Motion Style Using Parametric Bias

Kento Kawaharazuka, Yoichiro Kawamura, Kei Okada, Masayuki Inaba
RA-L 2021

Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory

Cited by 14Source ↗Scholar ↗
2020

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
IROS 2020poster

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 le…

Cited by 4Source ↗Scholar ↗