title: CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic
    Environments
summary: 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.
paper_authors._id: 67c6ab3ec0b62d612c54ddf5
paper_authors.name: Mingcong Lei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T08:34:48.061Z
paper_authors._id: 6628c6107751d297d7025a71
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6628c6107751d297d7025a71/S1rm5VIwV2Uxfv8GetKMU.jpeg
paper_authors.fullname: Lei Mingcong
paper_authors.type: user
paper_authors.user: SP4595
paper_authors._id: 67c6ab3ec0b62d612c54ddf6
paper_authors.name: Ge Wang
paper_authors._id: 67c6ab3ec0b62d612c54ddf7
paper_authors.name: Yiming Zhao
paper_authors._id: 67c6ab3ec0b62d612c54ddf8
paper_authors.name: Zhixin Mai
paper_authors._id: 67c6ab3ec0b62d612c54ddf9
paper_authors.name: Qing Zhao
paper_authors._id: 67c6ab3ec0b62d612c54ddfa
paper_authors.name: Yao Guo
paper_authors._id: 67c6ab3ec0b62d612c54ddfb
paper_authors.name: Zhen Li
paper_authors._id: 67c6ab3ec0b62d612c54ddfc
paper_authors.name: Shuguang Cui
paper_authors._id: 67c6ab3ec0b62d612c54ddfd
paper_authors.name: Yatong Han
paper_authors._id: 67c6ab3ec0b62d612c54ddfe
paper_authors.name: Jinke Ren
paper_title: CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic
    Environments
paper_summary: 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.
paper_ai_summary: Closed-Loop Embodied Agent (CLEA) enhances reliable task execution in dynamic environments using a hierarchical interactive planner and multimodal execution critic.
paper_ai_keywords: Large Language Models (LLMs) semantic reasoning embodied systems closed-loop task management interactive task planner multimodal execution critic probabilistic assessment hierarchical re-planning environmental memory real environment heterogenous robots task planning task execution robustness