{"ID":2836282,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21135","arxiv_id":"2511.21135","title":"SocialNav: Training Human-Inspired Foundation Model for Socially-Aware Embodied Navigation","abstract":"Embodied navigation that adheres to social norms remains an open research challenge. Our SocialNav is a foundational model for socially-aware navigation with a hierarchical \"brain-action\" architecture, capable of understanding high-level social norms and generating low-level, socially compliant trajectories. To enable such dual capabilities, we construct the SocNav Dataset, a large-scale collection of 7 million samples, comprising (1) a Cognitive Activation Dataset providing social reasoning signals such as chain-of-thought explanations and social traversability prediction, and (2) an Expert Trajectories Pyramid aggregating diverse navigation demonstrations from internet videos, simulated environments, and real-world robots. A multi-stage training pipeline is proposed to gradually inject and refine navigation intelligence: we first inject general navigation skills and social norms understanding into the model via imitation learning, and then refine such skills through a deliberately designed Socially-Aware Flow Exploration GRPO (SAFE-GRPO), the first flow-based reinforcement learning framework for embodied navigation that explicitly rewards socially compliant behaviors. SocialNav achieves +38% success rate and +46% social compliance rate compared to the state-of-the-art method, demonstrating strong gains in both navigation performance and social compliance. Our project page: https://amap-eai.github.io/SocialNav/","short_abstract":"Embodied navigation that adheres to social norms remains an open research challenge. Our SocialNav is a foundational model for socially-aware navigation with a hierarchical \"brain-action\" architecture, capable of understanding high-level social norms and generating low-level, socially compliant trajectories. To enable...","url_abs":"https://arxiv.org/abs/2511.21135","url_pdf":"https://arxiv.org/pdf/2511.21135v2","authors":"[\"Ziyi Chen\",\"Yingnan Guo\",\"Zedong Chu\",\"Minghua Luo\",\"Yanfen Shen\",\"Mingchao Sun\",\"Junjun Hu\",\"Shichao Xie\",\"Kuan Yang\",\"Pei Shi\",\"Zhining Gu\",\"Lu Liu\",\"Honglin Han\",\"Xiaolong Wu\",\"Mu Xu\",\"Yu Zhang\",\"Ning Guo\"]","published":"2025-11-26T07:36:01Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false}