Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System

• URL: http://arxiv.org/abs/2506.05020v3
• Date: Mon, 27 Oct 2025 04:26:01 GMT
• Title: Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System
• Authors: Haokun Liu, Zhaoqi Ma, Yunong Li, Junichiro Sugihara, Yicheng Chen, Jinjie Li, Moju Zhao,
• Abstract summary: Heterogeneous multirobot systems show great potential in complex tasks requiring coordinated hybrid cooperation.<n>Existing methods that rely on static or task-specific models often lack generalizability across diverse tasks and dynamic environments.<n>We propose a hierarchical multimodal framework that integrates a prompted large language model (LLM) with a fine-tuned vision-language model (VLM)
• Score: 8.88014241557266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heterogeneous multirobot systems show great potential in complex tasks requiring coordinated hybrid cooperation. However, existing methods that rely on static or task-specific models often lack generalizability across diverse tasks and dynamic environments. This highlights the need for generalizable intelligence that can bridge high-level reasoning with low-level execution across heterogeneous agents. To address this, we propose a hierarchical multimodal framework that integrates a prompted large language model (LLM) with a fine-tuned vision-language model (VLM). At the system level, the LLM performs hierarchical task decomposition and constructs a global semantic map, while the VLM provides semantic perception and object localization, where the proposed GridMask significantly enhances the VLM's spatial accuracy for reliable fine-grained manipulation. The aerial robot leverages this global map to generate semantic paths and guide the ground robot's local navigation and manipulation, ensuring robust coordination even in target-absent or ambiguous scenarios. We validate the framework through extensive simulation and real-world experiments on long-horizon object arrangement tasks, demonstrating zero-shot adaptability, robust semantic navigation, and reliable manipulation in dynamic environments. To the best of our knowledge, this work presents the first heterogeneous aerial-ground robotic system that integrates VLM-based perception with LLM-driven reasoning for global high-level task planning and execution.

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