一个世界模型，既当大脑又当手脚
机器人界一直分两派：一派让模型预测动作会带来什么变化(世界模型)，另一派让模型直接选动作(策略)。GreenWAM 把两件事合并成一个模型，还让它跨 20 种机器人身体通用——从双臂到人形，共用一套关节坐标。它当世界模型时预测轨迹和 3D 精度领先，当策略时在多个基准上追平专用模型，还成功装到真实人形机器人上，比从两个知名模型改来的策略成功率更高。关键做法是把动作专家从生成器里拆出来，以及训练时保留世界模型模式，让它在场景变化时更稳。这不是你明天能用的东西，但它指向一个方向：机器人可能不再需要为每个身体、每个任务单独训练模型，一个通用世界模型就能通吃。
📄 原文摘要(英文)
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.