---
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