Flexible Assimilation of Human's Target for Versatile Human-Robot Physical Interaction

摘要

Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that enables a contact robot to estimate the human's virtual target from the interaction force, and combine it with its own target to plan motion. While the virtual target depends on the control gains assumed for the human, we show that this does not affect the stability of the human-robot system, and our novel scheme covers a continuum of interaction behaviours from cooperation to competition. Simulations and experiments illustrate how the IAC can assist the human or compete with them to prevent collisions. In this article, we demonstrate the IAC's advantages over related methods, such as faster convergence to a target, guidance with less force, safer obstacle avoidance and a wider range of interaction behaviours.