Model Predictive Impedance Control with Gaussian Processes for Human and Environment Interaction

In tasks where the goal or configuration varies between iterations, human-robot interaction (HRI) can allow the robot to handle repeatable aspects and the human to provide information which adapts to the current state. Advanced interactive robot behaviors are currently realized by inferring human goal or, for physical interaction, adapting robot impedance. While many application-specific heuristics have been proposed for interactive robot behavior, they are often limited in scope, e.g. only considering human ergonomics or task performance. To improve generality, this paper proposes a framework which plans both trajectory and impedance online, handles a mix of task and human objectives, and can be efficiently applied to a new task. This framework can consider many types of uncertainty: contact constraint variation, uncertainty in human goals, or task disturbances. An uncertainty-aware task model is learned from a few demonstrations using Gaussian Processes. This task model is used in a nonlinear model predictive control (MPC) problem to optimize robot trajectory and impedance according to belief in discrete human goals, human kinematics, safety constraints, contact stability, and frequency-domain disturbance rejection. This MPC formulation is introduced, analyzed with respect to convexity, and validated in co-manipulation with multiple goals, a collaborative polishing task, and a collaborative assembly task.