CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic
  Environments
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.
67c6ab3ec0b62d612c54ddf5
Mingcong Lei
claimed_verified
2025-03-04T08:34:48.061Z
6628c6107751d297d7025a71
https://cdn-avatars.huggingface.co/v1/production/uploads/6628c6107751d297d7025a71/S1rm5VIwV2Uxfv8GetKMU.jpeg
Lei Mingcong
user
SP4595
67c6ab3ec0b62d612c54ddf6
Ge Wang
67c6ab3ec0b62d612c54ddf7
Yiming Zhao
67c6ab3ec0b62d612c54ddf8
Zhixin Mai
67c6ab3ec0b62d612c54ddf9
Qing Zhao
67c6ab3ec0b62d612c54ddfa
Yao Guo
67c6ab3ec0b62d612c54ddfb
Zhen Li
67c6ab3ec0b62d612c54ddfc
Shuguang Cui
67c6ab3ec0b62d612c54ddfd
Yatong Han
67c6ab3ec0b62d612c54ddfe
Jinke Ren
CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic
  Environments
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.
Closed-Loop Embodied Agent (CLEA) enhances reliable task execution in dynamic environments using a hierarchical interactive planner and multimodal execution critic.
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