Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu, Yingbo Tang, Hongsheng Li, Jinghui Lu, Xiu-shen Wei, Jiayi Ma, Yu Liu, Jing Zhang, Hangjun Ye, Xiaojun Liang, Long Chen, Wenbo Ding

TL;DR
This paper introduces Walk with Me, a map-free framework enabling robots to perform long-horizon, socially compliant outdoor navigation based on natural language instructions, leveraging vision-language models and safety reasoning.
Contribution
It presents a novel map-free approach combining semantic grounding, long-horizon planning, and safety-aware reasoning for outdoor social navigation from high-level instructions.
Findings
Successfully grounds high-level instructions into concrete destinations.
Achieves long-horizon navigation without relying on pre-built HD maps.
Handles complex situations with safety-aware decision making.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
