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arXiv · 2504.10030

EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control

Hanwen Wan·Yifei Chen·Yixuan Deng·Zeyu Wei·Dongrui Li·Zexin Lin·Donghao Wu·Jiu Cheng·Xiaoqiang Ji
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Abstract

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.

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Hanwen Wan, Yifei Chen, Yixuan Deng, Zeyu Wei, Dongrui Li, Zexin Lin, Donghao Wu, Jiu Cheng, Xiaoqiang Ji. 2025-04-14. EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control. https://arxiv.org/abs/2504.10030

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