EmoBipedNav: Emotion-Aware Social Navigation for Bipedal Robots With Deep Reinforcement Learning

This study presents an emotion-aware navigation framework—EmoBipedNav—using deep reinforcement learning (DRL) for bipedal robots walking in socially interactive environments. The inherent complex dynamics of bipedal robots challenge their safe maneuvering capabilities in dynamic environments. Furthermore, the intricacies of social interactions and cues such as emotions significantly compound the challenges of bipedal robot navigation. To address these coupled issues, we propose a two-stage pipeline that jointly considers the bipedal full-body dynamics and the complexities of socially aware navigation. More specifically, an emotion-integrated navigation policy is developed to balance safety, efficiency, and social courtesy by responding to human emotional cues. One key component of the policy is a novel representation of social environments using sequential LiDAR grid maps, from which we extract latent features, implicitly including collision regions, discomfort zones determined by emotions, social interactions, and the evolving dynamics of the navigation system including robot movements and pedestrian motions. To accounts for path tracking errors and locomotion constraints during social navigation, we present an end-to-end navigation system that incorporates full-order robot dynamics during training. Finally, extensive benchmarking and sim-to-real experiments demonstrate that our method outperforms both model-based planners and DRL baselines, and generalizes effectively to real-world environments.