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Datasets:
Forgis
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factorynet-hackathon

Tasks:
Time Series Forecasting
Robotics
Tabular Classification
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Languages:
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Size:
10M - 100M
Tags:
robotics
manufacturing
anomaly-detection
physical-ai
industrial
tool-wear
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metadata 
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) 

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) 

setpoint_vel_*       # Commanded velocities
feedback_vel_*       # Actual velocities
effort_force_x/y/z   # End-effector forces
ctx_temp_*           # Joint temperatures

Quick Start 

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:

@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 - CC BY 4.0
  • voraus-AD: GitHub - MIT License

License

CC BY 4.0 - Free to use with attribution.

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