EmboMatrix: A Scalable Training-Ground for Embodied Decision-Making
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915553130577920 |
|---|---|
| author | Lei, Zixing Yin, Sheng Xiong, Yichen Ding, Yuanzhuo Huang, Wenhao Wei, Yuxi Xu, Qingyao Li, Yiming Li, Weixin Wang, Yunhong Chen, Siheng |
| author_facet | Lei, Zixing Yin, Sheng Xiong, Yichen Ding, Yuanzhuo Huang, Wenhao Wei, Yuxi Xu, Qingyao Li, Yiming Li, Weixin Wang, Yunhong Chen, Siheng |
| contents | Embodied decision-making enables agents to translate high-level goals into executable actions through continuous interactions within the physical world, forming a cornerstone of general-purpose embodied intelligence. Large language models (LLMs), with their general decision-making capabilities, offer a promising path to realize this potential; however, LLMs trained solely on language lack exposure to physical environments, limiting their true embodied understanding. To bridge this gap, we propose the concept of a training ground: a comprehensive infrastructure that provides task and scene simulation, embodied interaction, and feedback signals, offering a one-stop solution for LLM acquire genuine embodied decision-making skills. In this work, we present EmboMatrix, the first training ground of its kind, providing massive and diverse tasks with efficient simulation and precise rewards. EmboMatrix incorporates a series of novel techniques: a multi-agent data engine for large-scale task and scene generation, a distributed heterogeneous-hardware system for scalable simulation, and a multi-level reward architecture for precise supervision. Leveraging EmboMatrix, we cultivate EmboBrain, an LLM whose embodied decision-making abilities emerge from extensive embodied interactions. Experiments show that EmboBrain-7B surpasses the 671B DeepSeek-R1 baseline by 9.5\% on two challenging embodied decision-making benchmarks, demonstrating the power of interactive, environment-grounded learning for building truly intelligent embodied agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmboMatrix: A Scalable Training-Ground for Embodied Decision-Making Lei, Zixing Yin, Sheng Xiong, Yichen Ding, Yuanzhuo Huang, Wenhao Wei, Yuxi Xu, Qingyao Li, Yiming Li, Weixin Wang, Yunhong Chen, Siheng Artificial Intelligence Robotics Embodied decision-making enables agents to translate high-level goals into executable actions through continuous interactions within the physical world, forming a cornerstone of general-purpose embodied intelligence. Large language models (LLMs), with their general decision-making capabilities, offer a promising path to realize this potential; however, LLMs trained solely on language lack exposure to physical environments, limiting their true embodied understanding. To bridge this gap, we propose the concept of a training ground: a comprehensive infrastructure that provides task and scene simulation, embodied interaction, and feedback signals, offering a one-stop solution for LLM acquire genuine embodied decision-making skills. In this work, we present EmboMatrix, the first training ground of its kind, providing massive and diverse tasks with efficient simulation and precise rewards. EmboMatrix incorporates a series of novel techniques: a multi-agent data engine for large-scale task and scene generation, a distributed heterogeneous-hardware system for scalable simulation, and a multi-level reward architecture for precise supervision. Leveraging EmboMatrix, we cultivate EmboBrain, an LLM whose embodied decision-making abilities emerge from extensive embodied interactions. Experiments show that EmboBrain-7B surpasses the 671B DeepSeek-R1 baseline by 9.5\% on two challenging embodied decision-making benchmarks, demonstrating the power of interactive, environment-grounded learning for building truly intelligent embodied agents. |
| title | EmboMatrix: A Scalable Training-Ground for Embodied Decision-Making |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2510.12072 |