SPLC: Social Preference Learning for Crowd Robot Navigation
For researchers in human-robot coexistence, SPLC provides a practical method to learn socially compliant navigation without handcrafted rewards, though it is an incremental improvement over existing offline RL approaches.
SPLC eliminates the need for detailed reward design in crowd robot navigation by introducing a social preference feedback mechanism that automatically generates preference data. It achieves consistent improvements over state-of-the-art baselines in both simulation and real-world TurtleBot4 experiments.
Offline reinforcement learning (RL) holds significant potential for crowd robot navigation in human-robot coexistence applications. However, the inherent complexity of pedestrian motion renders the design of effective reward functions for promoting socially compliant robot behaviors a persistent challenge. This paper proposes a Social Preference Learning for Crowd Robot Navigation (SPLC) algorithm to eliminate the need for detailed reward design. Its core innovation lies in the introduction of a social preference feedback mechanism to automatically generate preference data through principled preference evaluation criteria. By explicitly accounting for the intricacies of pedestrian dynamics, the pipeline mitigates the reward bias and facilitates the systematic quantification of broad social norms, thereby fostering socially compliant behaviors. Extensive experiments integrating SPLC with offline RL methods demonstrate consistent improvements over state-of-the-art baselines across standard performance metrics. Furthermore, real-world experiments on the TurtleBot4 further validate the effectiveness of SPLC in practical human-robot coexistence settings. Our code and video demos are available at https://github.com/sklus949/SPLC.