ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning
ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning
The ThinkWorld model enhances robot generalization by representing actions as dynamic chains and learning from unlabeled human videos using world imagination. This VLA framework represents each action reasoning as a dynamic reasoning chain (CoD), combining reinforcement learning with world imagination to reinforce reasoning, allowing unlabeled videos to provide direct supervision for state transitions, achieving a shared reasoning space for human and robot experiences. Experiments prove its effective implementation of human-robot transfer and generalization to unseen tasks and environments on both simulation and real robots.
Published on Oct 27
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Abstract
ThinkWorld improves robotic generalization by representing actions as dynamics chains and using world imagination to learn from unlabeled human videos.
Vision-Language-Action (VLA) models have made substantial progress in robotic manipulation, but their generalization remains constrained by the diversity and coverage of action-labeled robot demonstrations. Human videos provide much broader behavioral and environmental experience, while learning from them faces a fundamental trade-off between scalable data and explicit action grounding. We introduce ThinkWorld, a VLA learning framework that grounds action reasoning in world dynamics and strengthens it through reinforcement learning with world imagination. ThinkWorld represents each action thought as a Chain-of-Dynamics (CoD), a sequence of discrete motion tokens that captures the intermediate dynamics connecting the current world to a future state and serves as a common action representation across embodiments. We first initialize CoD with action-labeled demonstrations, then predict the future induced by each CoD and reinforce those whose predictions better match the observed outcome. This enables videos without action annotations to provide direct supervision from observed state transitions, allowing human and robot experience to refine the same reasoning space. Experiments in simulation and on real robots demonstrate effective human-to-robot transfer and strong generalization to unseen tasks and environments.
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