Failure Identification in Imitation Learning Via Statistical and Semantic Filtering
FIDeL is a policy-independent failure detection module for imitation learning policies that distinguishes between normal deviations and actual failures. It uses vision-based anomaly detection to find unusual states, aligns these states using optimal transport matching, and then employs a Vision-Language Model (VLM) for semantic filtering to accurately classify anomalies as genuine failures.