Russ Tedrake interprets the Ambient Diffusion Policy paper: the value of suboptimal data varies with noise level

Adam's new paper proposes Ambient Diffusion Policy, which selectively learns robot policies from suboptimal data through a data-usage approach that depends on noise level. The authors note that when training denoising diffusion models, each data point is used multiple times at different noise levels, and a data point's value may differ by noise level. The method can extract value from a considerable portion of the OXE dataset that was previously discarded, and is complementary to pi0.7's approach of learning conditioned on data quality.