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EVO-WAM：通过视频-动作验证让世界动作模型自我进化
AI 导读
EVO-WAM 让世界动作模型无需额外专家演示即可适应新任务，通过状态预测与锚定多帧上下文实现自回归 rollout，并用视觉语言模型筛选完成任务的前缀、以逆动力学模型验证视频-动作一致性后迭代训练。
正文
Authors:Shiyang Zhou, Xionghao Wu, Wenbo Li, Shenghe Zheng, Jiyao Zhang, Songsong Yu, Yijun Yang, Jianhui Liu, Haoze Sun, Senqiao Yang, Li Jiang, Jingyong Su, Haoyang Huang, Zhuotao Tian
Abstract:Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action models (WAMs) use broad video priors to jointly predict future videos and actions, offering a potential source of supervision for adapting to new tasks. However, generated videos may fail to depict task completion, and even visually successful videos may be paired with inconsistent actions that lead to execution failure. We propose EVO-WAM, a framework that adapts WAMs to unseen tasks by learning from their own generated video-action trajectories, without executing candidate actions in an external environment. First, we augment WAM training with state prediction and anchored multi-frame context to enable complete autoregressive rollouts without external execution feedback. Second, we identify reliable training experience by selecting task-completing prefixes with a vision-language model and verifying their video-action consistency with an inverse dynamics model. Third, we iteratively train the WAM on verified prefixes and generate new rollouts with the updated model. On seven unseen RoboTwin 2.0 tasks, EVO-WAM increases average success rates from 26.9% to 68.0% for Cosmos3 and from 28.5% to 46.4% for DreamZero, reaching approximately $2.5\times$ and $1.6\times$ their initial success rates. On three unseen long-horizon composite tasks in the real world, it improves Cosmos3's average success rate from 20.0% to 76.7%, a gain of 56.7 percentage points. Project Page: this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO) |
| Cite as: | arXiv:2609.38057 [cs.CV] |
| (or arXiv:2609.38057v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38057 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shiyang Zhou [view email]
[v1]
Tue, 29 Sep 2026 17:25:35 UTC (20,540 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org