Development of Low-Inertia Backdrivable Arm Focusing on Learning-Based Control

Manabu Nishiura, Akira Hanano, Kazutoshi Nishii, Yoshihiro Okumatsu

Abstract:

A robot designed to coexist and work with humans in the
same workspace should be able to work at the same speed
as humans and have safe contact with humans and with the
environment. However, when a robot arm has been given
flexibility through mechanisms and controls for the purpose
of coexistence, it is difficult for it to perform tasks at the speed
and accuracy desired by humans if it is moved simply by using
conventional position-based controls. With such an arm, we
consider that the use of learning-based control is necessary to
achieve both safety and speed. Therefore, we prototyped a low-
inertia, high-backdrivability arm as a platform for studying
learning-based control and tested two types of learning-based
control. This paper describes our design process, in which
hardware suitable for learning-based control was developed
according to the requirements of the specific task. It also
presents the results of our evaluation experiments, in which
tasks involving quick movements and motion requiring physical
contact with an object were performed using learning-based
control.