Haptic Assistance via Inverse Reinforcement Learning

In assistive teleoperation, an autonomous agent uses a prediction about a human user's intent to attempt to align the behavior of a controlled system with the human's goal, even if the human's own inputs are not perfectly aligned to that goal. Haptic Assistance achieves this effect by influencing the human through forces/torques applied to the human's control interface. In this work, we describe our method for creating such haptic assistance via Inverse Reinforcement Learning applied to successful task demonstrations. We then use our assistance method to examine the role that haptic feedback plays in assistive teleoperation. Through our user study, we find that when the assistance incorrectly predicts a user's intent, aiding the user via haptic feedback on their control interface, rather than directly modifying their input signal, is preferable and provides the user with a significantly greater sense of control over the system.