Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909037280362496 |
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| author | Levy, Jacob Westenbroek, Tyler Huang, Kevin Palafox, Fernando Yin, Patrick Omidshafiei, Shayegan Kim, Dong-Ki Gupta, Abhishek Fridovich-Keil, David |
| author_facet | Levy, Jacob Westenbroek, Tyler Huang, Kevin Palafox, Fernando Yin, Patrick Omidshafiei, Shayegan Kim, Dong-Ki Gupta, Abhishek Fridovich-Keil, David |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_15759 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation Levy, Jacob Westenbroek, Tyler Huang, Kevin Palafox, Fernando Yin, Patrick Omidshafiei, Shayegan Kim, Dong-Ki Gupta, Abhishek Fridovich-Keil, David Robotics Artificial Intelligence Machine Learning 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 |
| title | Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.15759 |