论文/arXiv 2610.23288·12 天前

CoEvolve：用于人形机器人的从技能到策略的自进化代码策略

CoEvolve将团队协作表示为固定运动技能上的可执行代码，并以验证引导演化，在双仿真Unitree G1搬运任务中验证。

AI 整理

• 硬件零部件
• 具身模型
• 评测与安全

宇树 G1

原文链接：CoEvolve: Self-Evolving Code Policies from Skills to Strategies for Humanoid Robots（arXiv 2610.23288 · 发布日期：2026-10-16）

CoEvolve: Self-Evolving Code Policies from Skills to Strategies for Humanoid Robots

CoEvolve将团队协作表示为固定运动技能上的可执行代码，并以验证引导演化，在双仿真Unitree G1搬运任务中验证。

核心要点

• 单机器人受本体限制，现有多机器人框架对执行偏离后的协调适应支持有限。
• CoEvolve在固定机器人运动技能上，用可执行代码表示团队协调。
• 多模态基础模型生成策略，物理验证逐步评估，并从已接受检查点修订。
• 15次基础运行完成8次，额外布局与经验初始化后可验证放置，但精度和接触稳定性受限。

验证范围与局限：验证范围为两台仿真Unitree G1在受限走廊中搬运自由支撑刚性载荷。

英文摘要 (Abstract)

Recent advances in Vision-Language-Action (VLA) models and World Action Models (WAMs) have expanded the manipulation and whole body capabilities of individual robots. Yet a single robot remains limited by its embodiment, and many real world tasks require capabilities that can only emerge through cooperation. Existing multi robot frameworks largely emphasize task decomposition, allocation, and replanning, while providing limited support for adapting executable coordination when physical execution deviates from the intended behavior. We present CoEvolve, a framework that represents team coordination as executable code over fixed robot motor skills. CoEvolve introduces Verifier Guided Coordination Evolution, where a multimodal foundation model instantiates coordination strategies through motion primitives, semantic objectives, temporary robot roles, and bounded execution parameters. Physical verification evaluates each executed transition, rejected behaviors trigger structured coordination revision from an accepted checkpoint, and verified experience is retained to guide subsequent decisions. We evaluate CoEvolve with two simulated Unitree G1 humanoids transporting a freely supported rigid payload through constrained corridors. Across 15 base task runs with three multimodal backbone families, CoEvolve completes 8 tasks, with every backbone achieving end to end success. Additional evaluations yield verified placement on two additional corridor layouts and after both same-backbone and cross-backbone experience initialization, while exposing persistent limitations in motion precision and contact stability. These results support executable coordination as a practical interface for adapting long horizon multi robot collaboration under shared physical constraints.

作者：Zicheng Liu, Haochen Wu, Jiawei He, Suyun Zhao, Huan Yang, Cheng Chi, Hao Tang

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.23288：CoEvolve: Self-Evolving Code Policies from Skills to Strategies for Humanoid Robots访问 2026-10-21