Risk-Sensitive Optimal Feedback Control for Haptic Assistance

While human behavior prediction can increase the capability of a robotic partner to generate anticipatory behavior during physical human robot interaction (pHRI), predictions in uncertain situations can lead to large disturbances for the human if they do not match the human intentions. In this paper we present a novel control concept in which the assistive control parameters are adapted to the uncertainty in the sense that a the robot takes a more or less active role depending on its confidence in the human behavior prediction. The approach is based on risk-sensitive optimal feedback control. The human behavior is modeled using probabilistic learning methods and any unexpected disturbance is considered as a source of noise. The proposed approach is validated in situations with different uncertainties, process noise and risk-sensitivities in a tow- Degree-of-Freedom virtual reality experiment.