Paper Detail
Wei He, Hengtao Li, Zhongrui Yu, Xuhan Zhu, Maokui He, Zide Liu, Xiyue Zhang, Xianwei Mao, Chunpeng Zhou, Jia Shi, Yanze Xin, Jingwen Li, Jingxie Zheng, Sijie Zeng, Chenfeng Wang, Fan Lu, Zeyu Zhang, Shuai Guo, Hengxuan Zhang, Pengfei Yu, Yu Liu, Kun Zhan, Yan Xie
Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical interaction. We introduce MachEmbodied-Brain (ME-Brain), a self-evolving embodied system organized around a closed loop of action execution, experience acquisition, experience evolution, and improved execution. Evolvable Memory consolidates multimodal trajectories into hierarchical, reusable experience; Cognitive Core transforms physical experience into transferable skills; and the Action Model combines event-driven keyframes, EventCell local-world prediction, and action-conditioned memory modulation to focus computation on decision-critical moments, regions, and historical evidence. Together, these modules shift embodied intelligence from train-and-freeze to deploy-and-evolve without model retraining. Cognitive Core outperforms the strongest comparison models by 8.2 and 9.6 points on embodied and agent benchmarks. The Action Model achieves 47.88% mean success on RoboMME, a 3.26-point improvement over the strongest baseline. On RoboDojo, it reaches a 21.51 mean Score and 16.03% success rate, exceeding $π_{0.5}$ by 10.10 and 9.12 points. On the six-task ME-RealBench, ME-Brain achieves a 69.5 mean Score and 66.7% success rate, outperforming DM0.5 by 12.8 and 11.7 points, respectively.
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@article{he2026me,
title = {ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence},
author = {Wei He and Hengtao Li and Zhongrui Yu and Xuhan Zhu and Maokui He and Zide Liu and Xiyue Zhang and Xianwei Mao and Chunpeng Zhou and Jia Shi and Yanze Xin and Jingwen Li and Jingxie Zheng and Sijie Zeng and Chenfeng Wang and Fan Lu and Zeyu Zhang and Shuai Guo and Hengxuan Zhang and Pengfei Yu and Yu Liu and Kun Zhan and Yan Xie},
year = {2026},
abstract = {Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical interaction. We introduce MachEmbodied-Brain (ME-Brain), a self-evolving embodied system organized around a closed loop of action execution, experience acquisition, experience evolution, and improved execution. Evolvable Memory consolidates multimodal trajectories into hierarchical, reusable experience; Cognitive Core transforms physical experience in},
url = {https://arxiv.org/abs/2609.24271},
keywords = {cs.RO},
eprint = {2609.24271},
archiveprefix = {arXiv},
}
{}