Shared Control Considering Human Decision Making and Motion Constraints

Abstract

We propose shared control that enables complicated and diverse motions. It is essential to support human operations by considering human decision making and motion constraints to realize complex motions. Furthermore, when various movements are targeted, it is necessary to judge the movement that the human wants. In this study, we assess the desired movement based on the affordances defined as probabilities. Furthermore, the reference motions are learned stochastically with Probabilistic Movement Primitives (ProMPs). It assists human operations by considering the human decision making and the motion constraints by the variances of the references. The proposed method was evaluated through experiments.