ThinkWorld模型通过世界想象强化动态推理能力
ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning
ThinkWorld模型通过将动作表示为动态链并利用世界想象从无标注人类视频中学习，提升了机器人泛化能力。该VLA框架将每个动作推理表示为动态推理链（CoD），通过强化学习与世界想象结合强化推理，使无标注视频能提供状态转换的直接监督，实现人类与机器人经验的共同推理空间。实验证明其在仿真和真实机器人上有效实现人机迁移并泛化到未见任务和环境。
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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来源: Hugging Face Daily Papers · huggingface.co