End-to-End Safe Social Navigation via Multi-Task Reinforcement Learning and Probabilistic Perception

Abstract:Autonomous social navigation requires balancing efficiency, physical safety, and social compliance. Reinforcement Learning (RL) methods provide a viable and effective solution but often rely on unrealistic assumptions, such as the knowledge of humans' position and velocity. In this paper, we introduce JESSI (JAX-based E2E Safe Social Interpretable navigation), a lightweight end-to-end RL framework that maps raw LiDAR scans directly to kinematically feasible control commands. JESSI enhances safety via Dirichlet-parameterized continuous action spaces and deterministic bounding, while an integrated attention-based perception module extracts probabilistic human states for interpretable, socially aware decision-making. Through extensive simulations and real-world deployment on a differential-drive robot, we demonstrate that jointly optimizing the RL policy with a supervised perception signal in a multi-task paradigm enhances social behavior. Ultimately, JESSI is able to balance high navigation success rates and superior social behaviors compared to state-of-the-art baselines.

Robotics (cs.RO); Human-Computer Interaction (cs.HC)

arXiv:2610.05733 [cs.RO]

(or arXiv:2610.05733v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.05733 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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