Dynamic Role Adaptation with Risk-Sensitive Optimal Feedback Control in Physical Human-Robot Interaction

Anticipatory behavior based on the human behavior prediction enables the robot to improve the quality of its assistance in physical human-robot interaction (pHRI). However, predictions are partly afflicted with high uncertainties originating from the intrinsic variability in human behavior and the influence of the environment which requires the negotiation among the partners. In this paper, we propose a novel control approach that dynamically adapts the robot’s role to uncertainties in real time facilitating the negotiation between the human and the robot. The approach is based on risksensitive optimal feedback control. The negotiation between the human and the robot is realized through a dynamical changing risk-sensitivity parameter. The proposed approach is experimentally validated in a cooperative transport scenario in a two-dimensional visuo-haptic virtual environment.