By grounding action reasoning in world dynamics rather than explicit action labels, robots can learn from unlabeled human videos alongside labeled robot data, dramatically expanding the training data available for robotic manipulation.
ThinkWorld is a framework that teaches robots to learn manipulation skills from both labeled robot videos and unlabeled human videos. It represents actions as sequences of motion tokens (Chain-of-Dynamics) that predict how the world will change, then uses reinforcement learning to strengthen predictions that match real outcomes.