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

• URL: http://arxiv.org/abs/2504.10030v1
• Date: Mon, 14 Apr 2025 09:33:42 GMT
• Title: EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control
• Authors: Hanwen Wan, Yifei Chen, Zeyu Wei, Dongrui Li, Zexin Lin, Donghao Wu, Jiu Cheng, Yuxiang Zhang, Xiaoqiang Ji,
• Abstract summary: EmbodiedAgent is a hierarchical framework for heterogeneous multi-robot control.<n>Our approach integrates a next-action prediction paradigm with a structured memory system to decompose tasks into executable robot skills.<n>We present MultiPlan+, a dataset of more than 18,000 annotated planning instances spanning 100 scenarios.
• Score: 4.163413782205929
• License: http://creativecommons.org/licenses/by/4.0/
• 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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