arXiv 2610.03391 · cs.CV · 2026-10-02
Native Action-Prior Learning from Videos for World Action Models
Zhaochong An, Fei Zhang, Menglin Jia, Duncan Frost, Zijian Zhou, Yikai Wang, Xudong Wang, Aditya Patel 等 13 人
Meta
在最新榜 #241 本站收录于 2026-10-21
一句话要点
NAVA-WAM 直接用无动作标注的视频预训练动作策略，降低世界-动作模型对机器人标注数据的依赖。
NAVA-WAM pretrains the action policy directly from observation-only videos, reducing reliance on action-labelled robot data.
阅读 arXiv 原文 PDF Hugging Face 讨论 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。