README.md

---

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