Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance

• URL: http://arxiv.org/abs/2604.26839v1
• Date: Wed, 29 Apr 2026 16:02:13 GMT
• Title: Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
• Abstract summary: Walk with Me is a map-free framework for long-horizon social navigation from high-level human instructions.<n>It combines semantic intent grounding, map-free long-horizon planning, safety-aware reasoning, and low-level action generation.
• Score: 70.01362005983516
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• G2-Nav: Grounded and Guarded Vision-Language Costmaps for Robot Social Navigation [28.188751747297715]
  Social navigation requires the robot to reason and respond in complex real-world environments.<n>We present G2-Nav, a novel framework that grounds abstract social reasoning and guards safe real-world deployment.<n>We demonstrate through real-world experiments that G2-Nav delivers safe, efficient, and socially compliant autonomous navigation in unstructured environments.
  arXiv  Detail & Related papers  (2026-07-18T20:18:20Z)
• ABot-N1: Toward a General Visual Language Navigation Foundation Model [27.085429358326504]
  ABot-N1 is a step toward a general Visual Language Navigation foundation model.<n>A slow vision-language reasoner performs explicit Chain-of-Thought reasoning while producing a pixel goal.<n>New benchmarks are released as open source to advance the field of urban-scale navigation.
  arXiv  Detail & Related papers  (2026-07-11T16:21:03Z)
• Think before Go: Hierarchical Reasoning for Image-goal Navigation [42.91450373979155]
  We propose a framework that decomposes image-goal navigation into high-level planning and low-level execution.<n>In high-level planning, a vision-language model is trained on a self-collected dataset to generate a short-horizon plan.<n>In low-level execution, an online reinforcement learning policy is utilized to decide actions conditioned on the short-horizon plan.
  arXiv  Detail & Related papers  (2026-04-19T12:30:22Z)
• OpenFrontier: General Navigation with Visual-Language Grounded Frontiers [54.661157616245966]
  Open-world navigation requires robots to make decisions in complex everyday environments.<n>Recent advances in vision--language navigation (VLN) and vision--language--action (VLA) models enable end-to-end policies conditioned on natural language.<n>We propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision--language prior models.
  arXiv  Detail & Related papers  (2026-03-05T17:02:22Z)
• Fast Navigation Through Occluded Spaces via Language-Conditioned Map Prediction [18.70552952249317]
  We introduce PaceForecaster as an approach that incorporates such co-pilot instructions into local planners.<n>PaceForecaster takes the robot's local sensor footprint and the provided co-pilot instructions as input.<n>We show that using language-conditioned forecasts and goals improves navigation performance by 36% over a local-map-only baseline while in polygonal environments.
  arXiv  Detail & Related papers  (2025-12-24T19:34:08Z)
• NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction [12.352236127154761]
  We introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination.<n>Our approach empowers a single VLM to concurrently perform planning and predictive foresight.<n>Our work highlights the immense potential of fusing explicit language planning with implicittemporal prediction, paving the way for more intelligent and capable embodied agents.
  arXiv  Detail & Related papers  (2025-12-01T11:24:16Z)
• CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory [39.76840258489023]
  Aerial vision-and-language navigation (VLN) requires drones to interpret natural language instructions and navigate complex urban environments.<n>We propose textbfCityNavAgent, a large language model (LLM)-empowered agent that significantly reduces the navigation complexity for urban aerial VLN.
  arXiv  Detail & Related papers  (2025-05-08T20:01:35Z)
• SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments [27.485636944766718]
  Large Language Models (LLMs) have demonstrated powerful contextual reasoning abilities over a range of robotic tasks.<n>SPINE is an online planner for missions with incomplete mission specifications provided in natural language.<n>We evaluate SPINE in simulation and real-world settings with missions that require multiple steps of semantic reasoning and exploration.
  arXiv  Detail & Related papers  (2024-10-03T22:41:47Z)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport [83.06265788137443]
  We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation. Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning. We propose a modular hierarchical transport policy (HTP) that builds a topological graph of the scene to perform exploration with the help of weighted frontiers.
  arXiv  Detail & Related papers  (2022-10-28T05:30:49Z)
• ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints [94.60414567852536]
  Long-range navigation requires both planning and reasoning about local traversability. We propose a learning-based approach that integrates learning and planning. ViKiNG can leverage its image-based learned controller and goal-directed to navigate to goals up to 3 kilometers away.
  arXiv  Detail & Related papers  (2022-02-23T02:14:23Z)
• Coupling Vision and Proprioception for Navigation of Legged Robots [65.59559699815512]
  We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot. We show superior performance compared to wheeled robot (LoCoBot) baselines. We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute.
  arXiv  Detail & Related papers  (2021-12-03T18:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.