TS-WAM: Temporal Latent Actions and World-Action Coupling for Vision-Language Robot Control
TS-WAM is a lightweight model for vision-language robot control that uses self-supervised temporal learning and explicit future prediction to learn from diverse data like robot demonstrations and human videos. It couples a latent action predictor with visual dynamics modeling to generate robust actions, achieving strong manipulation performance across various tasks.