Jinqiao Wang

Fixing world-action models to a single next-state representation fundamentally bottlenecks policy learning; supervising across dynamically sampled geometric, semantic, and visual projections of the same future allows robots to directly absorb dynamics from heterogeneous human video.

HarnessWAM allows robots to recover from failures and maintain knowledge across tasks, achieving unprecedented success rates in complex embodied environments.