DiscoDemo: Using reinforcement learning to generate diverse robot demonstration data to improve imitation learning

DiscoDemo uses reinforcement learning as a data generator, training a privileged-information latent-conditioned generator that discovers multiple successful solutions through a parallel reverse curriculum and a success-gated diversity objective, then exports them after safety filtering as a static dataset for downstream visuomotor imitation learning. On four manipulation tasks, the method raises downstream success in simulation from 40% for the strongest baseline to 62%, and on a real robot from 39% to 78%. The authors believe that the diversity of successful solutions can improve imitation learning for a pretrained flow-matching VLA, and that it complements corrective data collection.