Yue Su

Learning to predict the visual consequences of actions significantly boosts policy performance and generalization, while simultaneously learning to generate actions sharpens world models.

SG-WAM achieves unprecedented accuracy in action-conditioned future state prediction by integrating geometry-aware dynamics directly into the policy representation space.