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论文arXiv 2609.273401 个月前

通过 MCP 实现 LLM 驱动机器人导航的空间与语义推理 ​

针对LLM接入ROS导航的表示与复用难题，提出经MCP暴露的导航表示层，并在模拟室内三类任务中验证。

AI 整理
  • 感知与导航
  • 具身模型

原文链接：Spatial and Semantic Reasoning for LLM-Driven Robot Navigation via MCP（arXiv 2609.27340 · 发布日期：2026-09-23）

Spatial and Semantic Reasoning for LLM-Driven Robot Navigation via MCP

针对LLM接入ROS导航的表示与复用难题，提出经MCP暴露的导航表示层，并在模拟室内三类任务中验证。

核心要点 ​

  • ROS导航中的占据栅格等原始几何消息，难以直接为LLM提供空间或语义上下文。
  • 框架以非侵入方式连接LLM推理与ROS导航，并通过MCP提供标准可复用工具。
  • 视觉地图模块将占据栅格转为度量、姿态感知图像，语义模块记录带位姿的路标观察。
  • 在三类任务中，LLM后端实现超过97%地图覆盖，并能按自然语言选择导航目标。

验证范围与局限：验证范围为模拟室内环境中的自主建图、空间推理导航和语义推理导航。

英文摘要 (Abstract) ​

Large language models (LLMs) are increasingly used as natural-language interfaces for robotic systems, yet their integration with Robot Operating System (ROS)-based navigation remains limited by two gaps. First, navigation data such as occupancy grids are represented as raw geometric messages that are difficult for LLMs to use directly as spatial or semantic context. Second, adding LLM-driven capabilities often requires custom wrappers or robot-specific interfaces, limiting reuse across systems. To address these challenges, we propose a non-invasive framework that connects LLM reasoning with ROS-based navigation through a navigation-oriented representation layer, exposed through the Model Context Protocol (MCP) as standardized, reusable tools so that any MCP-compatible LLM can access them without robot-specific wrappers. The visual map modules transform occupancy grids into metric, pose-aware images for goal reasoning, while the semantic annotation modules record waypoint-level observations with robot poses. We evaluate the framework on three tasks: autonomous mapping, spatial reasoning-based navigation, and semantic reasoning-based navigation. The results show that the evaluated LLM backends use these representations to achieve over 97% map coverage and select spatial or semantic navigation targets from natural-language instructions in a simulated indoor environment. This demonstrates representation-mediated LLM navigation without modifying the existing ROS navigation stack.

作者：Jungsoo Lee, Jaegyun Park, Wansoo Kim

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2609.27340：Spatial and Semantic Reasoning for LLM-Driven Robot Navigation via MCP访问 2026-10-21