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).# LiRAnomaly：面向机器人拾取-放置作业的视觉异常数据集 1 概述 LiRAnomaly是一款标注型RGB图像序列数据集，采集自执行拾取-放置任务的Franka EMIKA协作机器人。数据集涵盖正常运行序列，以及工业操作场景中常见的四类安全关键异常样本。 总帧数：正常帧31642 | 异常帧5434 采集配置：静态RGB相机、恒定室内光照环境 授权协议：知识共享署名4.0国际许可（CC BY 4.0） 永久存档编号（DOI）：10.5281/zenodo.15694846 数据存储：文件托管于Google Drive，详细说明见第2节。 本数据集可支持机器人异常检测、持续学习与安全保障相关研究。 2 访问方式与文件夹结构 2.1 访问方式 可引用的元数据存档已留存于Zenodo（DOI见上文）。数据文件可通过以下链接下载：https://drive.google.com/drive/folders/1LltfOwVVPZj3zg4vVmwnIxaUMDF6Durs?usp=drive_link 2.2 文件夹布局 LiRAnomaly/ └─ dataset/ ├─ pnp_<id>/ # 正常序列 ├─ pnp_<id>_0/ # 正常序列 ├─ pnp_<id>_1/ # 类型1——视觉传感器遮挡 ├─ pnp_<id>_2/ # 类型2——抓取失败 ├─ pnp_<id>_3/ # 类型3——夹爪故障 └─ pnp_<id>_4/ # 类型4——路径遮挡 每个目录包含以下文件： ├─ *.png ├─ … └─ labels.csv # 0代表正常样本，1代表异常样本 labels.csv格式： <帧文件名>,<二进制标签> *.png,0 *.png,1 ... 3 异常类别 | 后缀 | 名称 | 描述 | |---|---|---| | _0 | 正常运行 | 无故障的标准拾取-放置作业 | | _1 | 视觉传感器遮挡 | 相机临时被遮挡或视野受阻 | | _2 | 抓取失败 | 位姿估计误差导致拾取失败 | | _3 | 夹爪故障 | 运输过程中物体意外脱落 | | _4 | 路径遮挡 | 轨迹或目标区域出现障碍物 | 4 引用方式 请引用随附的学术论文： bibtex @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 联系方式 如有疑问或问题反馈，请致信[e-mail]。 © 2025 — 依据CC BY 4.0协议发布，完整法律条文详见LICENSE文件。