Distribution ShiftExpert
Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift
This preprint presents a self-repairing recurrent ensemble policy that recovers from sensor shifts online without expert labels. The headline result is that random observation masking plus Kalman fusion and per-step RFLO updates lets simulated locomotion policies regain near pre-shift performance after a sudden sensor shift, while full-visibility ensembles collapse and do not recover. The work matters for unattended robots that must self-correct when sensors drift, fail, or become noisy.