Not All Actions Contribute Equally: Learning Precise Progress Estimation from Imprecise Demonstrations

Progress estimators play an essential role in providing dense reward signals for reinforcement learning in robotic manipulation, but accurately assessing task progress can be much challenging. Existing methods heavily rely on expert data and naively assume that all actions within a demonstration con