Can I join you? Extended Social Force Based Model with Predictive Control and Barrier Functions for Humanoid Group Repositioning Amidst Obstacles

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Article Summary

As social and service robots increasingly share interaction spaces in public, they may form groups while coordinating tasks or interacting with humans. Initial group formation alone is insufficient in dynamic settings, because it can be disrupted when a person or another agent joins, leaves, or passes through the group. Although group dynamics and repositioning have been widely studied in swarm robotics and virtual-agent settings, little attention has been given to socially appropriate repositioning behaviors in groups of humanoid robots. Existing social-force-based approaches provide a useful foundation for group behavior, but they have limited obstacle handling, and can produce unsafe behavior as environmental complexity increases. We propose a novel framework that modulates traditional Social Force, guided by Model Predictive Control (MPC) to plan ahead, bounded by Control Barrier Functions (CBF) to increase safety for robot groups repositioning behaviors. This approach aims to produce safer and socially aware group repositioning behaviors under various group formation disturbance. We evaluate the method in simulation through an ablation study and an online user study. The results show that our proposed method can produce smoother and safer repositioning behaviors in complex environments while preserving social appropriateness.

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