Chisato Nakashima — accepted AI-conference papers

2021

Robotic Learning From Advisory and Adversarial Interactions Using a Soft Wrist

Masashi Hamaya, Kazutoshi Tanaka, Yoshiya Shibata, Felix von Drigalski, Chisato Nakashima, Yoshihisa Ijiri

RA-L 2021

In this letter, we developed a novel learning framework from physical human-robot interactions. Owing to human domain knowledge, such interactions can be useful for facilitation of learning. However, applying numerous interactions for training data might place a burden on human users, particularly i

Cited by 6 Source ↗Scholar ↗

2020

A Compact, Cable-driven, Activatable Soft Wrist with Six Degrees of Freedom for Assembly Tasks

Felix von Drigalski, Kazutoshi Tanaka, Masashi Hamaya, Robert Lee, Chisato Nakashima, Yoshiya Shibata +1

IROS 2020poster

Physical softness has been proposed to absorb impacts when establishing contact with a robot or its workpiece, to relax control requirements and improve performance in assembly and insertion tasks. Previous work has focused on special end effector solutions for isolated tasks, such as the peg-in-hol…

Cited by 35 Source ↗Scholar ↗

2020

Learning Robotic Assembly Tasks with Lower Dimensional Systems by Leveraging Physical Softness and Environmental Constraints

Masashi Hamaya, Robert Lee, Kazutoshi Tanaka, Felix von Drigalski, Chisato Nakashima, Yoshiya Shibata +1

ICRA 2020poster

In this study, we present a novel control framework for assembly tasks with a soft robot. Typically, existing hard robots require high frequency controllers and precise force/torque sensors for assembly tasks. The resulting robot system is complex, entailing large amounts of engineering and maintena…

Cited by 34 Source ↗Scholar ↗

2020

Learning Soft Robotic Assembly Strategies from Successful and Failed Demonstrations

Masashi Hamaya, Felix von Drigalski, Takamitsu Matsubara, Kazutoshi Tanaka, Robert Lee, Chisato Nakashima +2

IROS 2020poster

Physically soft robots are promising for robotic assembly tasks as they allow stable contacts with the environment. In this study, we propose a novel learning system for soft robotic assembly strategies. We formulate this problem as a reinforcement learning task and design the reward function from h…

Cited by 24 Source ↗Scholar ↗