Learning Selective Acoustic Signaling for Social Robot Navigation in Heterogeneous Crowds

Active acoustic signaling can improve robot navigation in crowds, but methods based on a cooperative-response assumption may generate unnecessary signals near static pedestrians and misjudge signaling effectiveness when non-cooperative pedestrians approach. This study proposes heterogeneous interactive crowd navigation with acoustic signaling, a selective acoustic-signaling method for heterogeneous crowds. The proposed method models dynamic cooperative pedestrians, static non-responsive pedestrians, and approaching non-cooperative chasers. A differentiated reinforcement-learning social reward suppresses ineffective signaling in static-only neighborhoods, while a second training stage introduces non-cooperative chasers controlled by a standard optimal reciprocal collision avoidance policy with fixed parameters to improve motion avoidance when signaling cannot alter chaser motion. Experiments in corridor and square-hall environments used 500 test episodes per setting. The differentiated reward reduced total signaling rates by 41.2% and 47.0%, respectively, and reduced static-condition signaling rates to 0%, while largely preserving dynamic-condition signaling and navigation time. In complete heterogeneous scenarios, the proposed method achieved success rates of 0.94 and 0.91, collision rates of 0.02 and 0.04, and catch rates of 0.08 and 0.12. These results indicate improved signaling selectivity and navigation performance under the evaluated controlled simulation conditions involving predefined cooperative and non-responsive pedestrian behaviors.