Exploring Model Predictive Control to Generate Optimal Control Policies for HRI Dynamical Systems
This work addresses improving robot behavior in HRI scenarios, such as assistive tasks and social interactions, but is incremental as it applies existing MPC methods to new HRI data.
The paper tackled generating human-aware control policies for Human-Robot Interaction (HRI) using Model Predictive Control (MPC) with mixed integer constraints, resulting in a humanoid robot generating 25% more eye contact when maximizing connection, though no statistical difference in perceived connection was found.
We model Human-Robot-Interaction (HRI) scenarios as linear dynamical systems and use Model Predictive Control (MPC) with mixed integer constraints to generate human-aware control policies. We motivate the approach by presenting two scenarios. The first involves an assistive robot that aims to maximize productivity while minimizing the human's workload, and the second involves a listening humanoid robot that manages its eye contact behavior to maximize "connection" and minimize social "awkwardness" with the human during the interaction. Our simulation results show that the robot generates useful behaviors as it finds control policies to minimize the specified cost function. Further, we implement the second scenario on a humanoid robot and test the eye contact scenario with 48 human participants to demonstrate and evaluate the desired controller behavior. The humanoid generated 25% more eye contact when it was told to maximize connection over when it was told to maximize awkwardness. However, despite showing the desired behavior, there was no statistical difference between the participant's perceived connection with the humanoid.