arXiv 2508.208992025-08-28cs.RO

面向室内环境高效物体搜索的语言增强移动操作

Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments

School of Computation, Information and Technology (CIT), Technical University of Munich（计算、信息与技术学院（CIT），慕尼黑技术大学）；State Key Laboratory for Novel Software Technology and the School of Science and Technology, Nanjing University (Suzhou Campus)（新型软件技术国家重点实验室和南京大学科学技术学院（苏州校区））

Liding Zhang, Zeqi Li, Kuanqi Cai, Qian Huang, Zhenshan Bing, Alois Knoll

发表信息：: 2025 IEEE International Conference on Cyborg and Bionic Systems (CBS)

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AI总结：

本文提出语言增强的分层导航框架GODHS，利用大语言模型进行场景语义推理，并结合启发式运动规划器，在陌生室内环境中实现比传统非语义策略更高效的物体搜索。

摘要：

使机器人能够在复杂、非结构化环境中高效搜索并识别物体，对于从家庭辅助到工业自动化等多种应用至关重要。然而，传统场景表示通常仅捕获静态语义，缺乏可解释的上下文推理能力，限制了其在完全陌生环境中引导物体搜索的能力。为应对这一挑战，我们提出了一种语言增强的分层导航框架，该框架紧密集成语义感知与空间推理。我们的方法名为目标导向动态启发式引导分层搜索（GODHS），利用大语言模型（LLMs）推断场景语义，并通过多级决策层次引导搜索过程。通过在层次结构的每个阶段应用结构化提示和逻辑约束来实现推理的可靠性。针对移动操作的具体挑战，我们引入了一种基于启发式的运动规划器，该规划器结合极角排序与距离优先级，以高效生成探索路径。在Isaac Sim中的综合评估证明了我们框架的可行性，表明与传统的非语义搜索策略相比，GODHS能够以更高的搜索效率定位目标物体。网站和视频可在以下地址获取：https://drapandiger.github.io/GODHS

英文摘要：

Enabling robots to efficiently search for and identify objects in complex, unstructured environments is critical for diverse applications ranging from household assistance to industrial automation. However, traditional scene representations typically capture only static semantics and lack interpretable contextual reasoning, limiting their ability to guide object search in completely unfamiliar settings. To address this challenge, we propose a language-enhanced hierarchical navigation framework that tightly integrates semantic perception and spatial reasoning. Our method, Goal-Oriented Dynamically Heuristic-Guided Hierarchical Search (GODHS), leverages large language models (LLMs) to infer scene semantics and guide the search process through a multi-level decision hierarchy. Reliability in reasoning is achieved through the use of structured prompts and logical constraints applied at each stage of the hierarchy. For the specific challenges of mobile manipulation, we introduce a heuristic-based motion planner that combines polar angle sorting with distance prioritization to efficiently generate exploration paths. Comprehensive evaluations in Isaac Sim demonstrate the feasibility of our framework, showing that GODHS can locate target objects with higher search efficiency compared to conventional, non-semantic search strategies. Website and Video are available at: https://drapandiger.github.io/GODHS

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。