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We model Human-Robot-Interaction (HRI) scenarios as linear dynamical systemsand use Model Predictive Control (MPC) with mixed integer constraints togenerate human-aware control policies. We motivate the approach by presentingtwo scenarios. The first involves an assistive robot that aims to maximizeproductivity while minimizing the human's workload, and the second involves alistening humanoid robot that manages its eye contact behavior to maximize"connection" and minimize social "awkwardness" with the human during theinteraction. Our simulation results show that the robot generates usefulbehaviors as it finds control policies to minimize the specified cost function.Further, we implement the second scenario on a humanoid robot and test the eyecontact scenario with 48 human participants to demonstrate and evaluate thedesired controller behavior. The humanoid generated 25% more eye contact whenit was told to maximize connection over when it was told to maximizeawkwardness. However, despite showing the desired behavior, there was nostatistical difference between the participant's perceived connection with thehumanoid.

Exploring Model Predictive Control to Generate Optimal Control Policies for HRI Dynamical Systems