Differential Game With Motor Intent Prediction for Diverging Human Motion Plan

Abstract

When a human controls a robot directly or via teleoperation, incomplete information and unpredictable environmental conditions can lead to conflicts between their plans. Differential game theory (GT) offers a framework for optimal robotic assistance, but existing methods for identifying the human model require a shared plan. This article introduces an approach to deal with a diverging human plan by leveraging a neuromechanical model of their viscoelasticity to directly estimate their motion intent during movement. The viscoelastic gains can then be integrated into a GT framework to compute optimal contribution of the robot to the common motor task. We evaluated the proposed method in experiments, comparing it with fixed impedance control and nonoptimal variable impedance control. Results demonstrate stable interactions even in the presence of conflicting motion plans, and superior performance relative to these two alternative methods.