arXiv 2609.38057 · cs.CV · 2026-09-29
EVO-WAM: Evolving World Action Models through Video-Action Verification
Shiyang Zhou, Xionghao Wu, Wenbo Li, Shenghe Zheng, Jiyao Zhang, Songsong Yu, Yijun Yang, Jianhui Liu 等 14 人
Harbin Institute of Techonology, Shenzhen
在最新榜 #177 本站收录于 2026-10-21
一句话要点
EVO-WAM 让世界-动作模型从自己生成并通过校验的视频-动作轨迹中学习新任务，无需新示范。
EVO-WAM adapts world-action models to new tasks by learning from their own verified generated video-action trajectories.
阅读 arXiv 原文 PDF Hugging Face 讨论 代码仓库 项目页
主题与学科
同主题：机器人与具身
- Has GPT-6-Astra Achieved Embodied Intelligence? ▲459
- In-Context Learning for Robots: Methods and Applications ▲388
- HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents ▲313
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives ▲310
- Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience ▲179
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
要点由本站根据摘要自写，不转载摘要原文，请阅读原文。