ME-Brain-1.0：面向演化具身智能的记忆、认知与行动

中文摘要 AI 辅助

当前的具身系统在很大程度上依赖于预训练能力，这些能力在部署后保持不变，限制了它们从物理交互中学习的能力。我们提出了MachEmbodied-Brain（ME-Brain），一个自演化的具身系统，其组织围绕行动执行、经验获取、经验演化和改进执行这一闭环。可演化记忆将多模态轨迹整合为分层、可复用的经验；认知核心将物理经验转化为可迁移的技能；行动模型结合事件驱动的关键帧、EventCell局部世界预测和行动条件记忆调制，将计算聚焦于决策关键的时刻、区域和历史证据。这些模块共同将具身智能从“训练即冻结”转变为“部署即演化”，无需模型重训练。认知核心在具身和智能体基准上分别以8.2和9.6分的优势超越最强对比模型。行动模型在RoboMME上达到47.88%的平均成功率，比最强基线提升3.26个百分点。在RoboDojo上，它达到21.51的平均得分和16.03%的成功率，分别超过π0.5达10.10和9.12分。在六任务ME-RealBench上，ME-Brain达到69.5的平均得分和66.7%的成功率，分别优于DM0.5达12.8和11.7个百分点。

英文摘要

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.

发表机构

• Li Auto Inc（理想汽车）

机构由 AI 辅助整理，请以论文原文为准。