Iterated-Best-Response-based Model Predictive Control for Shared-Control in Teleoperation

We study shared control in teleoperation where a human and an automation act simultaneously on the same robotic dynamics through a haptic input device. The interaction is cast as a two-player constrained differential game with tracking and effort costs and shared state and input constraints, and approximated in real time via an iterated best-response coupling of model predictive control problems. An operator-side implementation in ROS 2 is evaluated in simulation with three human models and in a hardware-in-the-loop experiment with a real operator. Results show accurate tracking, balanced effort, and robust constraint satisfaction.