大模型
arxiv.org ·
CoAdapt：面向工业物联网机器人集群的 LLM 自适应协作感知框架
该论文提出 CoAdapt，一种用于工业物联网（IIoT）机器人集群的自适应协作感知框架，由大语言模型（LLM）充当运行时融合控制器，根据当前空间布局与网络状态，联合决定哪些机器人参与融合以及采用哪种融合算法。LLM 基于从原始 LiDAR 点云生成的结构化自然语言场景描述进行推理，无需任务特定训练，并可泛化到未见过的集群拓扑。在 OPV2V 基准的 25 个场景上评估，通信开销降低 38%，检测精度与静态基线方法相当。论文发表于 ACSOS 2026。
AI推荐理由 arXiv 论文，摘要明确给出方法（LLM 作为运行时融合控制器，决定参与机器人与融合算法）、实验基准（OPV2V，25 个场景）和量化结果（通信开销降低 38%，检测精度与静态基线相当），并注明发表于 ACSOS 2026，信息密度和可验证性较高。局限在于仅基于摘要，未提供代码或复现细节，且 38% 的通信节省与精度持平结论尚待同行评审与独立验证；LLM 在工业机器人集群中的实际部署效果未在摘要中体现。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.16852 (cs)
[Submitted on 15 Sep 2026]
Title:CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms
Authors:Houssam Hajj Hassan, Antonia Maria Masucci, Lynda Zitoune (L2S), Salah-Eddine Elayoubi (L2S)
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Abstract:Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspection. In such deployments, collaborative perception enables robots to share LiDAR observations and collectively construct a richer model of their environment than an individual agent could produce alone. However, industrial environments are dynamic spaces where robot positions shift continuously, network bandwidth fluctuates, and the marginal contribution of robots to perception quality varies at runtime. Existing collaborative perception approaches are designed for static participation assumptions and cannot adapt to these dynamics without sacrificing either detection precision or communication efficiency. This paper presents CoAdapt, an adaptive collaborative perception framework for IIoT robotic swarms in which a Large Language Model (LLM) serves as a runtime fusion controller, jointly deciding which robots participate in the fusion process and which fusion algorithm to apply based on the current spatial configuration and network state. The LLM reasons over structured natural language descriptions of the scene derived from raw LiDAR point clouds, requiring no taskspecific training and generalizing to unseen swarm topologies. Evaluated on the OPV2V benchmark across 25 scenarios, our approach achieves a 38% reduction in communication cost while maintaining detection precision comparable to static baseline approaches.
Subjects:
Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as:
arXiv:2609.16852 [cs.AI]
(or
arXiv:2609.16852v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.16852
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arXiv-issued DOI via DataCite (pending registration)
Journal reference:
The 7th IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2026), Sep 2026, Cesena, Italy
Submission history
From: Houssam Hajj Hassan [view email] [via CCSD proxy]
[v1]
Tue, 15 Sep 2026 08:44:08 UTC (1,126 KB)
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