84 citations · 90 across the 11 of their papers we have counts for
11 papers
Learning to Shape by Grinding: Cutting-surface-aware Model-based Reinforcement Learning
Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara
Object shaping by grinding is a crucial industrial process in which a rotating grinding belt removes material. Object-shape transition models are essential to achieving automation…
Foundational Policy Acquisition via Multitask Learning for Motor Skill Generation
Satoshi Yamamori, Jun Morimoto
In this study, we propose a multitask reinforcement learning algorithm for foundational policy acquisition to generate novel motor skills. \textcolor{\hcolor}{Learning the rich rep…
Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine +3
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must b…
Two are not always better than one: Role specialization is an important determinant of collaborative task performance
Asuka Takai, Qiushi Fu, Yuzuru Doibata +7
Collaboration frequently yields better results in decision making, learning, and haptic interactions than when these actions are performed individually. However, is collaboration a…
Phase Portraits as Movement Primitives for Fast Humanoid Robot Control
Guilherme Maeda, Okan Koc, Jun Morimoto
Currently, usual approaches for fast robot control are largely reliant on solving online optimal control problems. Such methods are known to be computationally intensive and sensit…
An Optimal Assistive Control Strategy based on User's Motor Goal Estimation
Jun-ichiro Furukawa, Jun Morimoto
In this study, we propose an optimal assistive control strategy that uses estimated user's movement intention as the terminal cost function. We estimate the movement intention by o…