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

• URL: http://arxiv.org/abs/2506.05020v2
• Date: Mon, 16 Jun 2025 05:10:40 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: We propose a hierarchical framework integrating a prompted Large Language Model (LLM) and a fine-tuned Vision Language Model (VLM)<n>The LLM decomposes tasks and constructs a global semantic map, while the VLM extracts task-specified semantic labels and 2D spatial information from aerial images to support local planning.<n>This is the first demonstration of an aerial-ground heterogeneous system integrating VLM-based perception with LLM-driven task reasoning and motion planning.
• Score: 7.266794815157721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heterogeneous multi-robot systems show great potential in complex tasks requiring hybrid cooperation. However, traditional approaches relying on static models often struggle with task diversity 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 framework integrating a prompted Large Language Model (LLM) and a GridMask-enhanced fine-tuned Vision Language Model (VLM). The LLM decomposes tasks and constructs a global semantic map, while the VLM extracts task-specified semantic labels and 2D spatial information from aerial images to support local planning. Within this framework, the aerial robot follows an optimized global semantic path and continuously provides bird-view images, guiding the ground robot's local semantic navigation and manipulation, including target-absent scenarios where implicit alignment is maintained. Experiments on real-world cube or object arrangement tasks demonstrate the framework's adaptability and robustness in dynamic environments. To the best of our knowledge, this is the first demonstration of an aerial-ground heterogeneous system integrating VLM-based perception with LLM-driven task reasoning and motion planning.

Related papers

• DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge [56.3802428957899]
  We propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling.<n>DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning.<n>Experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks.
  arXiv  Detail & Related papers  (2025-07-06T16:14:29Z)
• T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models [35.83717913117858]
  We introduce T-Rex, a Task-Adaptive Framework for Spatial Representation Extraction.<n>We show that our approach delivers significant advantages in spatial understanding, efficiency, and stability without additional training.
  arXiv  Detail & Related papers  (2025-06-24T10:36:15Z)
• Grounding Language Models with Semantic Digital Twins for Robotic Planning [6.474368392218828]
  We introduce a novel framework that integrates Semantic Digital Twins (SDTs) with Large Language Models (LLMs)<n>The proposed framework effectively combines high-level reasoning with semantic environment understanding, achieving reliable task completion in the face of uncertainty and failure.
  arXiv  Detail & Related papers  (2025-06-19T17:38:00Z)
• RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation [80.20970723577818]
  We introduce RoboCerebra, a benchmark for evaluating high-level reasoning in long-horizon robotic manipulation.<n>The dataset is constructed via a top-down pipeline, where GPT generates task instructions and decomposes them into subtask sequences.<n>Compared to prior benchmarks, RoboCerebra features significantly longer action sequences and denser annotations.
  arXiv  Detail & Related papers  (2025-06-07T06:15:49Z)
• EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks [24.41705039390567]
  EmbodiedVSR (Embodied Visual Spatial Reasoning) is a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning.<n>Our method enables zero-shot spatial reasoning without task-specific fine-tuning.<n>Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence.
  arXiv  Detail & Related papers  (2025-03-14T05:06:07Z)
• Navigating Motion Agents in Dynamic and Cluttered Environments through LLM Reasoning [69.5875073447454]
  This paper advances motion agents empowered by large language models (LLMs) toward autonomous navigation in dynamic and cluttered environments.<n>Our training-free framework supports multi-agent coordination, closed-loop replanning, and dynamic obstacle avoidance without retraining or fine-tuning.
  arXiv  Detail & Related papers  (2025-03-10T13:39:09Z)
• Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies.<n>Our findings are synthesized in Flex (Fly lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors.<n>We demonstrate the effectiveness of this approach on a quadrotor fly-to-target task, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation [69.01029651113386]
  Embodied-RAG is a framework that enhances the model of an embodied agent with a non-parametric memory system.<n>At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail.<n>We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries.
  arXiv  Detail & Related papers  (2024-09-26T21:44:11Z)
• Towards Open-World Grasping with Large Vision-Language Models [5.317624228510749]
  An open-world grasping system should be able to combine high-level contextual with low-level physical-geometric reasoning. We propose OWG, an open-world grasping pipeline that combines vision-language models with segmentation and grasp synthesis models. We conduct evaluation in cluttered indoor scene datasets to showcase OWG's robustness in grounding from open-ended language.
  arXiv  Detail & Related papers  (2024-06-26T19:42:08Z)
• Cognitive Planning for Object Goal Navigation using Generative AI Models [0.979851640406258]
  We present a novel framework for solving the object goal navigation problem that generates efficient exploration strategies. Our approach enables a robot to navigate unfamiliar environments by leveraging Large Language Models (LLMs) and Large Vision-Language Models (LVLMs)
  arXiv  Detail & Related papers  (2024-03-30T10:54:59Z)
• Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning [32.045840007623276]
  We introduce Robotic Vision-Language Planning (ViLa), a novel approach for long-horizon robotic planning. ViLa directly integrates perceptual data into its reasoning and planning process. Our evaluation, conducted in both real-robot and simulated environments, demonstrates ViLa's superiority over existing LLM-based planners.
  arXiv  Detail & Related papers  (2023-11-29T17:46:25Z)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)
• Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation [87.03299519917019]
  We propose a dual-scale graph transformer (DUET) for joint long-term action planning and fine-grained cross-modal understanding. We build a topological map on-the-fly to enable efficient exploration in global action space. The proposed approach, DUET, significantly outperforms state-of-the-art methods on goal-oriented vision-and-language navigation benchmarks.
  arXiv  Detail & Related papers  (2022-02-23T19:06:53Z)

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.