A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies
Co-training, which combines limited in-domain real-world data with abundant surrogate data such as simulation or cross-embodiment robot data, is widely used for training generative robot policies. Despite its empirical success, the mechanisms that determine when and why co-training is effective remain poorly understood. We investigate the mechanism of sim-and-real co-training through theoretical analysis and empirical study, and identify two intrinsic effects governing performance. The first, \textbf{``structured representation alignment"}, reflects a balance between cross-domain representation alignment and domain discernibility, and plays a primary role in downstream performance. The se...
| Authors | Yu Lei, Minghuan Liu, Abhiram Maddukuri et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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