Learning Human-Robot Interaction for Robot-Assisted Pedestrian Flow Optimization

Due to the fast-is-slower phenomenon in emergency escape, it is desirable to regulate pedestrian flows at the exit or a bottleneck. We propose a new robot-assisted pedestrian regulation and study passive human-robot interaction (HRI). A learning-based motion control approach is presented for a robot to efficiently interact with pedestrians for desirable collective motion. We first formulate the problem into an optimal control framework using the pedestrian dynamics description based on existing social force models with embedded HRI forces. To solve the defined optimal control problem, we propose an adaptive dynamic programming (ADP) approach to provide adjustable motion parameters of the robot to efficiently interact with pedestrians so that the regulated pedestrian flow tracks a desired velocity. The ADP control process only uses observed flow information rather than the models of pedestrians, and the ADP method provides feedback control with online learning and control capability. Simulation results demonstrate that the proposed approach can regulate pedestrian flows to desirable speeds by online learning.