Token-World: World Modeling in Vision-Language Model Token Space for Robot Manipulation
Token-World simulates robot dynamics directly within a compact visual-token space used by Vision-Language Models, bypassing slow RGB image generation. It models future states as a combination of compressed visual tokens and proprioception using flow matching. This approach improves how well the simulator predicts future actions and increases the correlation between simulation success and actual policy performance.