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

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.