EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912538632912896 |
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| author | Wan, Hanwen Chen, Yifei Deng, Yixuan Wei, Zeyu Li, Dongrui Lin, Zexin Wu, Donghao Cheng, Jiu Ji, Xiaoqiang |
| author_facet | Wan, Hanwen Chen, Yifei Deng, Yixuan Wei, Zeyu Li, Dongrui Lin, Zexin Wu, Donghao Cheng, Jiu Ji, Xiaoqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control Wan, Hanwen Chen, Yifei Deng, Yixuan Wei, Zeyu Li, Dongrui Lin, Zexin Wu, Donghao Cheng, Jiu Ji, Xiaoqiang Robotics Artificial Intelligence 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. |
| title | EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.10030 |