外观
论文arXiv 2610.1877415 天前
GreenWAM：面向机器人的世界基础模型
原文链接：GreenWAM: A World Foundation Model for Robotics（arXiv 2610.18774 · 发布日期：2026-10-13）
GreenWAM: A World Foundation Model for Robotics
GreenWAM用共享解剖动作空间与联合训练，构建可跨不同本体兼作世界模型和策略的机器人模型，并在多项基准和真实人形机器人上验证。
核心要点
- 动机是探索一个机器人世界模型能否跨不同本体同时承担场景预测与动作选择。
- 方法包括覆盖20个机器人家族的共享解剖动作空间、多模式联合训练和分离动作专家的策略设计。
- 作为世界模型，GreenWAM在WorldArena 1.0的轨迹、深度和3D精度达到顶尖水平。
- 作为策略，其在RoboTwin 2.0、LIBERO-plus、CALVIN和LIBERO上表现靠前，并适配真实人形机器人。
验证范围与局限：验证范围包括多个仿真/基准任务以及一个真实人形机器人适配实验。
英文摘要 (Abstract)
A robot needs a world model that predicts how its actions will change the scene and a policy that chooses those actions. Models are usually built and evaluated for only one of these roles, and every robot records its actions in its own format. We ask whether one robot world model can serve both roles across different bodies, and what makes this work. We present GreenWAM, a world foundation model for robotics, together with the recipe that produces it: (1) a shared anatomical action space that describes 20 robot families, from bimanual arms to dexterous humanoids, in common joint coordinates; (2) joint training on these families in visual, action-conditioned and world-action modes; and (3) policies that split an action expert off the generator. As a world model, GreenWAM attains top-tier trajectory, depth and 3D accuracy on WorldArena 1.0, ahead of every world-action model and video generator in our comparison. As a policy, it scores among the top entries of the RoboTwin 2.0 leaderboard, reaches the highest zero-shot LIBERO-plus success among the compared methods and is competitive with dedicated vision-language-action and world-action policies on CALVIN and LIBERO. We further adapt GreenWAM to a real humanoid, where it succeeds more often than policies adapted from $π_{0.5}$ and from Cosmos 3 Edge, and distill it into a single-step policy for real-time control. Controlled experiments yield three findings: robot specialization improves world prediction and control together; an action expert helps when split off at the start of adaptation; and keeping world-model modes during policy adaptation preserves robustness to scene changes.
作者：Gennady Sidorov, Valerii Kornilov, Denis Grankin, Polina Fedotova, Aleksey Postnikov
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