Robust Imitation Learning from Corrupted Demonstrations

Liu Liu, Ziyang Tang, Lanqing Li +1

We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers. Classical approaches such as Behavio…