A robot learning a new task from 25 human video demonstrations, with its model weights frozen: that’s one result from Cornell’s Unified Motion-Action (UMA) model. Adaptation updates only a small set of task tokens that represent what to do. UMA connects human videos to robot control through 3D object motion: how points on objects move over time. It learns motion from videos without robot action labels, and learns the connection between motion and actions from robot data. Pretraining combines human videos, real robot demonstrations, and simulation. In video-only adaptation, UMA’s success rate was approximately 25 percentage points higher than UVA’s on each task: utensil insertion, sweeping, and folding jeans. Each task had 20 evaluation trials. The human demonstrations came from the evaluation scene and viewpoint. The practical takeaway is that a new task can be taught through human demonstrations without collecting robot action labels for that task. Prior robot training still supplies the action knowledge; deploying on a new robot embodiment still requires compatible robot data. How far does video-only adaptation transfer when the demonstrations come from a different scene or viewpoint? That’s the next test I’d want to see.