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.
@article{arxiv.2604.26839,
title = {Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance},
author = {Lingfeng Zhang and Xiaoshuai Hao and Xizhou Bu and Yingbo Tang and Hongsheng Li and Jinghui Lu and Xiu-shen Wei and Jiayi Ma and Yu Liu and Jing Zhang and Hangjun Ye and Xiaojun Liang and Long Chen and Wenbo Ding},
journal= {arXiv preprint arXiv:2604.26839},
year = {2026}
}