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README.md

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