STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning
STEAM is a self-supervised framework for robot learning that predicts future actions by analyzing expert demonstrations offline, without needing manual rewards or annotations. It uses an ensemble of predictors trained on temporal offsets between frames to create a conservative advantage score. This method improves policy performance significantly and helps localize where robots stall or fail during real-world tasks.