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

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 sarp.yenicesu@bilkent.edu.tr.

© 2025 — Released under CC BY 4.0 (see the LICENSE file for the full legal code).