Gaussian Process Dynamic Programming for Optimizing Ungrounded Haptic Guidance

Abstract

Adapting robot actions to human motions can make human-robot interactions (HRI) more effective. Here, we aim to optimize guidance from haptic devices based on a user's response to produce better task performance. We used Gaussian processes to model the motions a human user made in response to applied torques from an ungrounded control moment gyroscope haptic device. We then used Gaussian process dynamic programming to generate optimized haptic cues to guide the user to rotate the device toward 3D targets. We compared the performance of naive and optimized policies in simulations and with a human user, and found that dynamic programming can significantly improve haptic guidance in cases where human responses are highly variable or inconsistent with the cued haptic direction.