用仿真环境预训练机器人世界模型，实现实时快速适应。
Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
- 用物理仿真器生成动作条件数据，蒸馏结构先验到世界模型
- 真实世界仅微调动态模型，保留长时序规划能力
- 适合接触密集型任务，比传统方法更快更稳定
机器人学习需要能在有限、混合质量交互数据下可靠改进的适应方法。这在长时序、高接触任务中尤为困难，因为端到端策略微调效率低且脆弱。世界模型提供替代方案：通过预测候选动作序列的结果，实现基于反事实推理的在线规划。然而，在真实世界中直接训练动作条件的机器人世界模型需海量多样数据，不切实际。我们提出模拟蒸馏（SimDist）框架，利用物理模拟器作为可扩展的动作条件机器人经验来源。预训练阶段，SimDist从模拟器中蒸馏结构先验至世界模型，使其能从原始真实观测中进行规划。真实世界适应阶段，SimDist迁移模拟中学习的编码器、奖励模型和价值函数，仅用真实世界预测损失更新隐状态动态模型。这将适应过程简化为监督系统辨识，同时保留密集的长时序规划信号以支持在线优化。在接触密集型操作和四足行走任务中，SimDist能快速随经验提升，而先前方法在在线微调中进展缓慢甚至退化。项目网站与代码：https://sim-dist.github.io
原文摘要 · Abstract (English)
Robot learning requires adaptation methods that improve reliably from limited, mixed-quality interaction data. This is especially challenging in long-horizon, contact-rich tasks, where end-to-end policy finetuning remains inefficient and brittle. World models offer a compelling alternative: by predicting the outcomes of candidate action sequences, they enable online planning through counterfactual reasoning. However, training action-conditioned robotic world models directly in the real world requires diverse data at impractical scale. We introduce Simulation Distillation (SimDist), a framework that uses physics simulators as a scalable source of action-conditioned robot experience. During pretraining, SimDist distills structural priors from the simulator into a world model that enables planning from raw real-world observations. During real-world adaptation, SimDist transfers the encoder, reward model, and value function learned in simulation, and updates only the latent dynamics model using real-world prediction losses. This reduces adaptation to supervised system identification while preserving dense, long-horizon planning signals for online improvement. Across contact-rich manipulation and quadruped locomotion tasks, SimDist rapidly improves with experience, while prior adaptation methods struggle to make progress or degrade during online finetuning. Project website and code: https://sim-dist.github.io
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