CrossWAM: Imagine, Execute, Refine for Zero-Shot Cross-Embodiment Manipulation

Generalist robot policies, including vision-language-action (VLA) models and world-action models (WAMs), are typically pre-trained on data from many embodiments and then fine-tuned for one fixed robot, camera, and control interface. This coupling makes a trained policy difficult to reuse once the robot morphology or the camera configuration changes, even when the task and the underlying object dynamics are unchanged. We therefore study zero-shot cross-embodiment transfer, in which a policy trained on a single source embodiment must control robots never seen during training, under camera configurations never seen either, without collecting any target demonstration. Our starting point is that, how a task should unfold (how objects move and how contacts form) is largely independent of the robot that executes it, whereas the source robot's appearance is tightly bound to one particular arm and camera. Building on this observation, CrossWAM makes three components. First, we make the imagined future of a world-action model embodiment-agnostic, removing the source arm by video inpainting and re-expressing its motion as an abstract gripper trace. Second, because a viewpoint change reprojects the same world state onto different pixels and thereby biases the predicted actions, we close the loop with an imagination–observation refiner that compares the imagined future against what actually happened after execution and corrects the actions not yet executed. Third, an asynchronous denoising schedule assigns fewer denoising steps to near-future frames, reducing first-video latency by 48.5%. On the bimanual RoboTwin 2.0 benchmark, which jointly varies embodiment and camera pose, CrossWAM improves success rate by up to 22.1% over the strongest prior method, and real-robot experiments on two platforms confirm that it transfers under simultaneous embodiment and viewpoint shift.