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ThinkWorld:世界的想象力强化了动态推理能力

原文标题 · ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning

作者:Chaehyun Kim, Chi-Pin Huang, Jisoo Kim | 视觉-语言-动作模型在机器人操控领域取得了显著进展,但其泛化能力仍然受到带有动作标签的机器人演示数据多样性和覆盖范围的限制。人类视频提供了更丰富的行为和环境信息,但从这些视频中学习时,需要权衡可扩展的数据资源与明确的动作信息之间的平衡。我们提出了一种新的方法……

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Robotics

Title: ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning

Abstract: 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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