Disagreement-aware physical assistance through risk-sensitive optimal feedback control

Proactive physical robotic assistance in the presence of human prediction uncertainty is a very challenging control problem. In this paper we propose a risk-sensitive optimal feedback controller for physical assistance that autonomously adapts the robot's behavior even during unknown situations. Using a probabilistic model to represent the cooperative task execution behavior and modeling the human as a source of process noise in the system, the proposed assistive controller proactively contributes to the task anticipating the human motion. Estimating online the current level of disagreement and prediction uncertainty, the assistive controller consequently calculates the optimal task contribution providing higher adaptability. A psychological evaluation compares different assistive control strategies in a virtual scenario using a two-Degree-of-Freedom haptic experimental setup. Results show that considering the current level of disagreement enhances the performance of the controller in terms of helpfulness and human effort minimization.