arXiv cs.AI· Sepideh Ravanbakhsh, Hamidreza Moradi·· 10 小时前AI 评分31
LG-HRL：面向多智能体搜救的语言锚定分层强化学习框架
Cognitive Multi-Agent Search and Rescue: Language-Grounded Hierarchical Reinforcement Learning for Collaborative Robotics
AI 导读
研究者提出基于语言锚定分层强化学习（LG-HRL）的认知多智能体框架，由 LLM 将人类指令转为结构化语义线索，指导 UAV 与 UGV 的高层策略选择与角色分配。在模拟灾难环境中，该系统学习速度提升 2.3 倍、协调一致性提高 37%、子目标准确率与危险规避分别提升 46% 和 31%，受害者救援率高出 20-30%。消融实验确认共享认知工作区与语言锚定对整体性能均不可或缺。
正文 · 原文
Abstract:Effective search and rescue (SAR) operations require autonomous systems capable of coordinating multiple heterogeneous agents, adapting to dynamic hazards, and aligning with human mission intent. Existing language-guided reinforcement learning (RL) methods primarily focus on single-agent instruction following and lack mechanisms for team-level coordination in complex, partially observable environments. This paper presents a Cognitive Multi-Agent Framework based on Language-Grounded Hierarchical Reinforcement Learning (LG-HRL) that integrates natural-language understanding, hierarchical policy decomposition, and a shared cognitive workspace to achieve cohesive multi-agent behavior in SAR scenarios. Human instructions are interpreted by a large language model (LLM) and transformed into structured semantic cues that guide high-level strategy selection and role assignment across UAV and UGV agents. A formal Language-Grounded Multi-Agent Hierarchical Decision Process (LG-MAHDP) is introduced to model the interplay between semantic grounding, hierarchical control, and distributed coordination. Comprehensive experiments in a simulated disaster environment show that LG-HRL improves learning speed by 2.3 times, increases coordination coherence by 37%, and enhances subgoal accuracy and hazard avoidance by 46% and 31%, respectively. The system achieves 20-30% higher victim rescue rates, demonstrates emergent role specialization, and adapts immediately to human verbal updates. Ablation studies confirm that both the shared cognitive workspace and language grounding are critical to overall system performance. Overall, this work establishes a new paradigm for language-guided multi-agent SAR robotics, demonstrating that semantic grounding combined with hierarchical reasoning enables safer, more efficient, and more interpretable autonomous rescue operations.
| Comments: | 33 pages, 8 Figures |
| Subjects: | Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2611.13161 [cs.AI] |
| (or arXiv:2611.13161v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13161 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hamidreza Moradi [view email]
[v1]
Mon, 9 Nov 2026 19:45:28 UTC (6,236 KB)
来源：arXiv cs.AI · arxiv.org