Model predictive game control for personalized and targeted interactive assistance

Contact robots are increasingly used to assist humans in physical training and manufacturing tasks. However, their effectiveness is currently limited as control methods focus on system performance without considering the upcoming human user's control. Here, we present a differential game-based controller for contact robots ensuring optimal interaction by predicting human motor control over their finite planning horizon. Using this model-predictive game (MPG) controller, we investigated human-robot co-adaptation in experiments, demonstrating that: (a) MPG interaction remains stable while reducing human effort; (b) the robot adapts to humans, identifying time-consistent individual interaction behaviors; (c) humans adapt to the robot, and their behavior can be modulated through an assistance meta-parameter adjusting the robot's propensity to minimize human effort. These findings indicate that humans understand and adapt to a partner's control strategy aligning with game theory principles. Furthermore, the assistance meta-parameter's ability to guide humans toward specific interaction behaviors enables versatile robot-assisted systems for physical training and rehabilitation.