Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.01516 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911640565317632 |
|---|---|
| author | Gu, Xunjiang Chitta, Kashyap Golchoubian, Mahsa Suplin, Vladimir Gilitschenski, Igor |
| author_facet | Gu, Xunjiang Chitta, Kashyap Golchoubian, Mahsa Suplin, Vladimir Gilitschenski, Igor |
| contents | Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulators provide only in isolation. We introduce Sim2Sim2Sim, a framework that bridges high-fidelity vehicle simulation and scalable reinforcement learning by distilling simulator dynamics into a highly parallelizable learned dynamics model. By training control policies purely within this distilled environment and deploying them back into the high-fidelity source simulator, we demonstrate more efficient policy optimization and reliable transfer under challenging dynamics. We further show that predictive accuracy alone does not fully characterize a learned dynamics model's suitability as a reinforcement learning training environment, which should also be assessed by the quality of the policies it enables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01516 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamics Distillation for Efficient and Transferable Control Learning Gu, Xunjiang Chitta, Kashyap Golchoubian, Mahsa Suplin, Vladimir Gilitschenski, Igor Robotics Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulators provide only in isolation. We introduce Sim2Sim2Sim, a framework that bridges high-fidelity vehicle simulation and scalable reinforcement learning by distilling simulator dynamics into a highly parallelizable learned dynamics model. By training control policies purely within this distilled environment and deploying them back into the high-fidelity source simulator, we demonstrate more efficient policy optimization and reliable transfer under challenging dynamics. We further show that predictive accuracy alone does not fully characterize a learned dynamics model's suitability as a reinforcement learning training environment, which should also be assessed by the quality of the policies it enables. |
| title | Dynamics Distillation for Efficient and Transferable Control Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.01516 |