SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when datasets are contaminated with suboptimal or noisy demonstrations. While prior quality-assessment methods attempt to filter or reweight data, they typi