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Description

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 risk-sensitive optimal feedback controller for haptic assistance. The human behavior is modeled using probabilistic learning methods and any unexpected disturbance is considered as a source of noise. The controller considers the inherent uncertainty of the probabilistic model and the process noise in the dynamics in order to adapt the behavior of the robot accordingly. The proposed approach is evaluated in situations with different uncertainties, process noise and risk-sensitivities in a 2 Degree-of-Freedom virtual reality setup.

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