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

翻译：机器人学习需要一种能够从有限且质量参差的交互数据中可靠提升性能的自适应方法。这在长时域、高接触任务中尤为困难——端到端策略微调效率低下且极不稳定。世界模型提供了极具竞争力的替代方案：通过预测候选动作序列的结果，世界模型能够利用反事实推理实现在线规划。然而，直接在现实世界中训练基于动作条件的机器人世界模型，需要规模难以企及的多样化数据。我们提出"模拟蒸馏"（SimDist）框架——该框架利用物理仿真器作为可扩展的、基于动作条件的机器人经验来源。在预训练阶段，SimDist将仿真器中的结构化先验蒸馏至世界模型，使其能基于原始现实观测进行规划。在现实世界自适应阶段，SimDist迁移仿真环境中学习到的编码器、奖励模型与价值函数，仅通过现实世界中的预测损失更新潜态动力学模型。这种设计将自适应过程简化为有监督的系统辨识，同时保留密集的长时域规划信号用于在线优化。在接触丰富的操控任务与四足机器人运动任务中，SimDist能随经验快速提升性能，而现有自适应方法却难以取得进展，甚至在线微调阶段出现性能退化。项目网站及代码：https://sim-dist.github.io