用大模型驱动机器人在商场导航购物，自动规划路线并抓取物品。
LLM-Based Agentic Exploration for Robot Navigation & Manipulation with Skill Orchestration
- 大模型在路口决策方向和进店，通过模块化动作执行
- 结合AprilTag定位与路标识别，构建轻量语义地图
- 全程可追踪决策日志，适合研究人机交互与自主导航
本文提出一种端到端的基于大语言模型的智能体探索系统，用于室内购物任务，在Gazebo仿真与真实走廊场景中均进行了评估。机器人通过检测路口标识牌，逐步构建轻量级语义地图，存储方向至兴趣点（POI）的关系及估计的路口位姿；AprilTag提供可重复使用的定位锚点以实现精准接近与对齐。给定自然语言购物指令后，大模型在每个路口生成受限的离散动作（方向与是否进店），由ROS有限状态主控制器通过门控模块化运动原语执行，包括基于局部代价地图的避障、AprilTag接近、进店与抓取。定性结果显示，该集成系统能从用户指令到多店导航与物品取回实现端到端任务执行，同时通过文本地图与记录的决策历史保持模块化与可调试性。
原文摘要 · Abstract (English)
This paper presents an end-to-end LLM-based agentic exploration system for an indoor shopping task, evaluated in both Gazebo simulation and a corresponding real-world corridor layout. The robot incrementally builds a lightweight semantic map by detecting signboards at junctions and storing direction-to-POI relations together with estimated junction poses, while AprilTags provide repeatable anchors for approach and alignment. Given a natural-language shopping request, an LLM produces a constrained discrete action at each junction (direction and whether to enter a store), and a ROS finite-state main controller executes the decision by gating modular motion primitives, including local-costmap-based obstacle avoidance, AprilTag approaching, store entry, and grasping. Qualitative results show that the integrated stack can perform end-to-end task execution from user instruction to multi-store navigation and object retrieval, while remaining modular and debuggable through its text-based map and logged decision history.
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