This repository hosts the project webpage and supplementary materials for the paper:
Failure Identification in Imitation Learning via Statistical and Semantic Filtering
Quentin Rolland, Fabrice Mayran de Chamisso, Jean-Baptiste Mouret
IEEE International Conference on Robotics and Automation (ICRA), 2026
link to the site : https://cea-list.github.io/FIDeL/
Imitation Learning (IL) policies are brittle to rare or out-of-distribution events in real-world robotic deployments.
We introduce FIDeL, a policy-agnostic failure identification framework that combines:
- Vision-based anomaly detection
- Optimal transport alignment with expert demonstrations
- Spatio-temporal thresholding via conformal prediction
- Semantic filtering using Vision-Language Models (VLMs)
FIDeL detects, localizes, and semantically filters failures in real time, without interfering with policy execution.
We also introduce BotFails, a multimodal dataset for robotic failure detection:
- Vision, proprioception, and language instructions
- 646 video sequences
- 414,359 annotated frames
- Real-world manipulation and interaction tasks
- Explicit failure and benign anomaly annotations
FIDeL outperforms state-of-the-art anomaly detection baselines on BotFails, achieving:
- +5.30% AUROC in anomaly detection
- +17.38% accuracy in failure identification
Qualitative results and videos are available on the project webpage.
Parts of this project page were adopted from the Nerfies page.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
