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, \t

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ethics.ai (15 April 2026), “A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies,” evidence record 5838, https://ethics.ai/record/5838 (originally published by arXiv).

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