CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic
  Environments
Authors: name: Mingcong Lei, name: Ge Wang, name: Yiming Zhao, name: Zhixin Mai, name: Qing Zhao, name: Yao Guo, name: Zhen Li, name: Shuguang Cui, name: Yatong Han, name: Jinke Ren

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.