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Ditch VAEs and AMP: SLMP learns structured motion priors in a spherical latent space, enabling stable random sampling of diverse and valid humanoid behaviors without information loss.
Generate superhuman humanoid motion data at scale by closing the loop between policy performance and data difficulty, surpassing limitations of fixed datasets.
