This paper introduces EmbodiedAgent, a hierarchical framework for heterogeneous multi-robot control. EmbodiedAgent addresses critical limitations of hallucination in impractical tasks. Our approach integrates a next-action prediction paradigm with a structured memory system to decompose tasks into executable robot skills while dynamically validating actions against environmental constraints. We present MultiPlan+, a dataset of more than 18,000 annotated planning instances spanning 100 scenarios, including a subset of impractical cases to mitigate hallucination. To evaluate performance, we propose the Robot Planning Assessment Schema (RPAS), combining automated metrics with LLM-aided expert grading. Experiments demonstrate EmbodiedAgent's superiority over state-of-the-art models, achieving 71.85% RPAS score. Real-world validation in an office service task highlights its ability to coordinate heterogeneous robots for long-horizon objectives.

本研究提出了具身代理，一个用于异构多机器人控制的分层框架，旨在解决在不切实际任务中出现的幻觉问题。该方法通过整合下一步动作预测范式和结构化记忆系统，将任务分解为可执行的机器人技能，同时动态验证与环境约束相符的行动。实验表明，具身代理在现实场景中的应用有效地协调了异构机器人，达到了71.85%的评估得分，展示了其在长远目标中的潜力。

具身代理：一种可扩展的分层方法以克服多机器人控制中的实际挑战