Egocentric videoExpert
World Action Learning via Interaction-Centric Spectral Latent Guidance
This preprint introduces WING, a framework that learns interaction-centric latent actions from egocentric human videos and transfers them to robot policies through low-frequency spectral guidance. In simulation it reaches 99.20% average success on LIBERO, 93.80% on RoboTwin 2.0, and 57.7% on RoboCasa–GR1, and on real-world bimanual tasks it achieves 75.0% average success in the standard setting. It matters because it offers a scalable path to use abundant egocentric video instead of costly robot teleoperation for robot learning.