| ---
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| license: cc-by-4.0
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| task_categories:
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| - time-series-classification
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| - question-answering
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| tags:
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| - robotics
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| - manufacturing
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| - anomaly-detection
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| - physical-ai
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| - industrial
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| size_categories:
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| - 1M<n<10M
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| language:
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| - en
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| pretty_name: FactoryNet Hackathon Dataset
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| ---
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|
|
| # FactoryNet Hackathon Dataset
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|
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| A unified multi-robot time-series dataset for industrial anomaly detection and Physical AI research.
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|
|
| ## Dataset Description
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|
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| FactoryNet unifies multiple robot operation datasets into a common schema for training anomaly detection and reasoning models. This release includes:
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|
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| | Dataset | Robot | Task | Episodes | Signals | Faults |
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| |---------|-------|------|----------|---------|--------|
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| | **AURSAD** | UR3e (6-DOF) | Screwdriving | 4,094 | 134 | 5 types |
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| | **voraus-AD** | Yu-Cobot (6-DOF) | Pick-and-place | 2,122 | 137 | 12 types |
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|
|
| ## FactoryNet Schema
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|
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| All datasets are converted to a unified schema with causal structure:
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|
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| ```
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| Intent (setpoint) → Action (effort) → Outcome (feedback)
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| ```
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|
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| ### Core Columns (Tier 1 - Universal)
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| ```python
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| setpoint_pos_0..N # Commanded joint positions (rad)
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| effort_torque_0..N # Motor torque/current (Nm / A)
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| feedback_pos_0..N # Actual joint positions (rad)
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| timestamp # Seconds since episode start
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| ```
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|
|
| ### Common Columns (Tier 2)
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| ```python
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| setpoint_vel_* # Commanded velocities
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| feedback_vel_* # Actual velocities
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| effort_force_x/y/z # End-effector forces
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| ctx_temp_* # Joint temperatures
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| ```
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|
|
| ## Quick Start
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|
|
| ```python
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| from datasets import load_dataset
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|
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| # Load AURSAD subset
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| ds = load_dataset("forgis/factorynet-hackathon", data_dir="aursad")
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|
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| # Access time series
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| df = ds['train'].to_pandas()
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| print(df[['timestamp', 'setpoint_pos_0', 'effort_torque_0', 'feedback_pos_0']].head())
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| ```
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|
|
| ## Minimum Viable Episode (MVE)
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|
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| Every episode in FactoryNet satisfies the **Minimum Viable Episode** constraint:
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| - ≥1 setpoint signal (commanded intent)
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| - ≥1 effort signal (motor response)
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|
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| This enables causal analysis: if `effort` doesn't follow `setpoint`, something is wrong.
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|
|
| ## Fault Types
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|
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| | Code | Description | Example |
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| |------|-------------|---------|
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| | `normal` | Normal operation | - |
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| | `stiff_joint` | Increased joint friction | AURSAD: damaged_thread |
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| | `collision` | Contact with obstacle | voraus: can_collision |
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| | `grip_failure` | Gripper malfunction | voraus: vacuum_loss |
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| | `missing_part` | Expected part absent | AURSAD: missing_screw |
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| | `tool_wear` | Progressive degradation | PHM2010: flank_wear |
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|
|
| ## File Structure
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|
|
| ```
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| forgis/factorynet-hackathon/
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| ├── aursad/
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| │ ├── aursad_factorynet.parquet # Time series (14K rows sample)
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| │ ├── aursad_extensions.parquet # Dataset-specific columns
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| │ └── aursad_metadata.json # Episode metadata
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| ├── voraus/
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| │ ├── voraus_ad_100hz_factorynet.parquet
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| │ └── voraus_ad_100hz_metadata.json
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| └── schema.json # FactoryNet schema reference
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| ```
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|
|
| ## Use Cases
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|
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| 1. **Anomaly Detection**: Train classifiers to detect faulty operations
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| 2. **Fault Diagnosis**: Identify which component/joint is failing
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| 3. **Remaining Useful Life**: Predict when tool/component will fail
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| 4. **Sim2Real Transfer**: Use real data to calibrate simulators
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| 5. **Robot Q&A**: Answer natural language questions about robot state
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|
|
| ## Citation
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|
|
| If you use this dataset, please cite:
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|
|
| ```bibtex
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| @dataset{factorynet2026,
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| title={FactoryNet: A Unified Dataset for Industrial Robot Anomaly Detection},
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| author={Forgis AI},
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| year={2026},
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| publisher={HuggingFace},
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| url={https://huggingface.co/datasets/forgis/factorynet-hackathon}
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| }
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| ```
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|
|
| ## Source Datasets
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|
|
| This dataset unifies and standardizes:
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| - **AURSAD**: [Zenodo](https://zenodo.org/records/4487073) - CC BY 4.0
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| - **voraus-AD**: [GitHub](https://github.com/vorausrobotik/voraus-ad-dataset) - MIT License
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|
|
| ## License
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|
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| CC BY 4.0 - Free to use with attribution.
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|
|
| ## Contact
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|
|
| - Forgis AI: hackathon@forgis.com
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| - Physical AI Hackathon: Zurich, Feb 28 - Mar 1, 2026
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|
|
