From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposes a benchmark for testing robustness of policy learning methods to shifts such as lighting, camera view, orientation. They observe that some of the current imitation-based policy learning techniques are not robust. The reviewers agree that the proposed environments could be a good testing ground for future work. The paper is well-written, though the reviewers note that they should cover more methods to make the rather general claim more credible.