GreenWAM: a robot world model that also acts as policy
GreenWAM is a robot world foundation model presented with the recipe that produces it: a shared anatomical action space describing 20 robot families, from bimanual arms to dexterous humanoids, in common joint coordinates; joint training across those families in visual, action-conditioned and world-action modes; and policies that split an action expert off the generator. As a world model it reports top-tier trajectory, depth and 3D accuracy on WorldArena 1.0, ahead of every world-action model and video generator in the comparison, and 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. The authors also adapt GreenWAM to a real humanoid, where it succeeds more often than policies adapted from π0.5 and Cosmos 3 Edge, and distill it into a single-step policy for real-time control.
If one world model can both predict scenes and serve as the policy, training and transfer costs for robot foundation models could fall, though these results come from the authors' own technical report.