RAISE: Learning from Failures by Reweighting Successful Robot Demonstrations

Learning from demonstrations is a cornerstone of general-purpose robotic manipulation. Yet success-centered imitation often underuses failure experience, while monotonic progress supervision overlooks local setbacks and retries within successful executions. We introduce RAISE (Risk-Aware Imitation from Successful Experience), a data-centric framework that turns failures into learning priorities rather than imitation targets. RAISE adapts a stage-aware progress model using only trajectories that reach stable completion, with retry supervision capturing local degradation and recovery. The frozen model grounds failure localization in stage- and progress-matched successful experience through temporally corroborated action deviations, changes in success support, and progress regression. Successful retries and localized failure onsets yield risk anchors that guide offline reweighting of existing demonstrations, emphasizing segments with similar pre-action states and positive predicted task advancement. Simulation and real-robot experiments show that RAISE substantially improves overall task success across diverse imitation learners, including conventional behavior cloning, diffusion policies, and vision–language–action models, without changing policy architectures or imitation targets.