We present BEAST, a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-spline.

We propose FIPER, a framework to predict failures of generative imitation learning policies by detecting consecutive OOD observations and high action uncertainty, achieving more accurate and faster detection than prior approaches.