Topic
Imitation Learning
Reward Learning under Distribution Shift
Uses variation across demonstrators as structural information to discourage reward features that fail under dynamics and nuisance shifts.
Evaluated reward transfer across five MuJoCo tasks under dynamics interventions.
Distribution-Matching Imitation Learning
Develops distribution-matching formulations of imitation learning, including sliced-Wasserstein objectives for occupancy matching.
Remained effective under up to 100× demonstration subsampling on Ant and Humanoid.