LiRAnomaly: Visual Anomaly Dataset for Robotic Pick‑and‑Place Operations

LiRAnomaly: Visual Anomaly Dataset for Robotic Pick‑and‑Place Operations 1 Overview LiRAnomaly is a labelled RGB image‑sequence dataset collected on a Franka EMIKA collaborative robot while performing pick‑and‑place tasks. It comprises both nominal operation runs and four classes of safety‑critical anomalies that frequently occur in industrial manipulation scenarios. Total frames: 31 642 normal | 5 434 anomalous Acquisition setup: static RGB camera, constant indoor lighting License: Creative Commons Attribution 4.0 International (CC BY 4.0) Permanent record (DOI): 10.5281/zenodo.15694846 Data storage: files are hosted on Google Drive – see Section 2. The dataset supports research in robotic anomaly detection, continual learning, and safety assurance. 2 Access & Folder Structure 2.1 Access A citable metadata record is preserved at Zenodo (DOI above).The data files themselves can be downloaded from:https://drive.google.com/drive/folders/1LltfOwVVPZj3zg4vVmwnIxaUMDF6Durs?usp=drive_link 2.2 Folder Layout LiRAnomaly/ └─ dataset/ ├─ pnp_<id>/ # Normal sequence ├─ pnp_<id>_0/ # Normal sequence ├─ pnp_<id>_1/ # Type 1 – visual sensor occlusion ├─ pnp_<id>_2/ # Type 2 – grasp failure ├─ pnp_<id>_3/ # Type 3 – gripper malfunction └─ pnp_<id>_4/ # Type 4 – path obstruction Each directory contains ├─ *.png ├─ … └─ labels.csv # 0 = normal, 1 = anomaly labels.csv format <frame_filename>,<binary_label> *.png,0 *.png,1 ... 3 Anomaly Categories Suffix Name Description _0 Normal operation Nominal pick‑and‑place without incident _1 Visual sensor occlusion Camera temporarily blinded or view blocked _2 Grasp failure Pose‑estimation error causes failed pickup _3 Gripper malfunction Unintended object release during transport _4 Path obstruction Obstacle appears in trajectory or target area 4 How to Cite Please cite the accompanying manuscript: @article{nourmohammadi2024locally, title = {Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell_p$-Norm Constraints for Robust Anomaly Detection}, author = {Nourmohammadi, Sepehr and Yenicesu, Arda Sarp and Rahimzadeh Arashloo, Shervin and Oguz, Ozgur S.}, journal = {arXiv preprint arXiv:2411.06406}, year = {2024}, note = {Manuscript under review at \textit{Pattern Recognition}; citation subject to change} } 5 Contact For questions or bug reports, please email [e-mail]. © 2025 — Released under CC BY 4.0 (see the LICENSE file for the full legal code).