Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917447667286016 |
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| author | Zhang, Lingfeng Hao, Xiaoshuai Bu, Xizhou Tang, Yingbo Li, Hongsheng Lu, Jinghui Wei, Xiu-shen Ma, Jiayi Liu, Yu Zhang, Jing Ye, Hangjun Liang, Xiaojun Chen, Long Ding, Wenbo |
| author_facet | Zhang, Lingfeng Hao, Xiaoshuai Bu, Xizhou Tang, Yingbo Li, Hongsheng Lu, Jinghui Wei, Xiu-shen Ma, Jiayi Liu, Yu Zhang, Jing Ye, Hangjun Liang, Xiaojun Chen, Long Ding, Wenbo |
| contents | Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation behavior. Existing map-based methods rely on costly pre-built HD maps, while learning-based policies are mostly limited to indoor and short-horizon settings. To bridge this gap, we propose Walk with Me, a map-free framework for long-horizon social navigation from high-level human instructions. Walk with Me leverages GPS context and lightweight candidate points-of-interest from a public map API for semantic destination grounding and waypoint proposal. A High-Level Vision-Language Model grounds abstract instructions into concrete destinations and plans coarse waypoint sequences. During execution, an observation-aware routing mechanism determines whether the Low-Level Vision-Language-Action policy can handle the current situation or whether explicit safety reasoning from the High-Level VLM is needed. Routine segments are executed by the Low-Level VLA, while complex situations such as crowded crossings trigger high-level reasoning and stop-and-wait behavior when unsafe. By combining semantic intent grounding, map-free long-horizon planning, safety-aware reasoning, and low-level action generation, Walk with Me enables practical outdoor social navigation for human-centric assistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26839 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance Zhang, Lingfeng Hao, Xiaoshuai Bu, Xizhou Tang, Yingbo Li, Hongsheng Lu, Jinghui Wei, Xiu-shen Ma, Jiayi Liu, Yu Zhang, Jing Ye, Hangjun Liang, Xiaojun Chen, Long Ding, Wenbo Robotics Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation behavior. Existing map-based methods rely on costly pre-built HD maps, while learning-based policies are mostly limited to indoor and short-horizon settings. To bridge this gap, we propose Walk with Me, a map-free framework for long-horizon social navigation from high-level human instructions. Walk with Me leverages GPS context and lightweight candidate points-of-interest from a public map API for semantic destination grounding and waypoint proposal. A High-Level Vision-Language Model grounds abstract instructions into concrete destinations and plans coarse waypoint sequences. During execution, an observation-aware routing mechanism determines whether the Low-Level Vision-Language-Action policy can handle the current situation or whether explicit safety reasoning from the High-Level VLM is needed. Routine segments are executed by the Low-Level VLA, while complex situations such as crowded crossings trigger high-level reasoning and stop-and-wait behavior when unsafe. By combining semantic intent grounding, map-free long-horizon planning, safety-aware reasoning, and low-level action generation, Walk with Me enables practical outdoor social navigation for human-centric assistance. |
| title | Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.26839 |