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arXiv · 2601.00555

LLM-Based Agentic Exploration for Robot Navigation & Manipulation with Skill Orchestration

Abu Hanif Muhammad Syarubany·Farhan Zaki Rahmani·Trio Widianto
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Abstract

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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Abu Hanif Muhammad Syarubany, Farhan Zaki Rahmani, Trio Widianto. 2026-01-02. LLM-Based Agentic Exploration for Robot Navigation & Manipulation with Skill Orchestration. https://arxiv.org/abs/2601.00555

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