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By keeping the pretrained diffusion policy frozen, ExpertGen regularizes exploration to remain within safe, human-like behavior manifolds, while also enabling e","title":"ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors","url":"https://arxiv.org/abs/2603.15956","vendor":"arxiv_cs_ai"},"summary":"arXiv:2603.15956v3 Announce Type: replace-cross \nAbstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (e.g., through teleoperation) serve as the standard source for expert behaviors, acquiring such data at scale in the real world is prohibitively expensive. This paper introduces ExpertGen, a framework that automates expert policy learning in simulation to enable scalable sim-to-real transfer. 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