Teaching the user by learning from the user: personalizing movement control in physical human-robot interaction

This paper proposes a novel approach for physical human-robot interactions U+0028 pHRI U+0029, where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction U+0028 p2HRI U+0029 method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration U+0028 LfD U+0029 techniques to adapt to the users U+02BC performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user, is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method, and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.