Dynamic strategy selection for physical robotic assistance in partially known tasks

Abstract

It is well-known that physical robotic assistance to humans is significantly enhanced by including human behavior anticipation into robot planning and control. The challenge arises when the human goal/plan is uncertain or unknown to the robot. In this paper we propose a novel control scheme which dynamically selects between a model-based and a model-free strategy depending on the level of disagreement between the human and the robot. The disagreement is measured in terms of the interaction force. A task specific model-based controller is selected when the human's motion intention coincides with the robot's goal. A model-free control scheme based on the human force as motion prediction source is selected in case of disagreement and when the human goal/plan is unknown. The benefits of this approach are demonstrated in a human user study on human-robot cooperative object transport through a 2D maze in virtual reality.