CLEA：閉環具身代理，用於提升動態環境中的任務執行效能

CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic Environments

March 2, 2025

作者: Mingcong Lei, Ge Wang, Yiming Zhao, Zhixin Mai, Qing Zhao, Yao Guo, Zhen Li, Shuguang Cui, Yatong Han, Jinke Ren

cs.AI

摘要

大型語言模型（LLMs）在通過語義推理對複雜任務進行層次分解方面展現出卓越的能力。然而，其在具身系統中的應用面臨著確保子任務序列可靠執行和實現長期任務一次性成功的挑戰。為了解決這些在動態環境中的局限性，我們提出了閉環具身代理（CLEA）——一種新穎的架構，結合了四個專門的開源LLMs，並通過功能解耦實現閉環任務管理。該框架具有兩大核心創新：（1）互動式任務規劃器，基於環境記憶動態生成可執行的子任務；（2）多模態執行評判器，採用評估框架對行動可行性進行概率評估，當環境擾動超過預設閾值時觸發層次重規劃機制。為了驗證CLEA的有效性，我們在一個包含可操作物體的真實環境中進行了實驗，使用兩台異構機器人執行物體搜索、操作以及搜索-操作整合任務。在12次任務試驗中，CLEA優於基準模型，成功率提高了67.3%，任務完成率提升了52.8%。這些結果表明，CLEA顯著增強了動態環境中任務規劃與執行的魯棒性。

English

Large Language Models (LLMs) exhibit remarkable capabilities in the hierarchical decomposition of complex tasks through semantic reasoning. However, their application in embodied systems faces challenges in ensuring reliable execution of subtask sequences and achieving one-shot success in long-term task completion. To address these limitations in dynamic environments, we propose Closed-Loop Embodied Agent (CLEA) -- a novel architecture incorporating four specialized open-source LLMs with functional decoupling for closed-loop task management. The framework features two core innovations: (1) Interactive task planner that dynamically generates executable subtasks based on the environmental memory, and (2) Multimodal execution critic employing an evaluation framework to conduct a probabilistic assessment of action feasibility, triggering hierarchical re-planning mechanisms when environmental perturbations exceed preset thresholds. To validate CLEA's effectiveness, we conduct experiments in a real environment with manipulable objects, using two heterogeneous robots for object search, manipulation, and search-manipulation integration tasks. Across 12 task trials, CLEA outperforms the baseline model, achieving a 67.3% improvement in success rate and a 52.8% increase in task completion rate. These results demonstrate that CLEA significantly enhances the robustness of task planning and execution in dynamic environments.