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Forgis
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factorynet-hackathon

Tasks:
Time Series Forecasting
Robotics
Tabular Classification
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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
  - tool-wear
  - cnc
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 industrial operation datasets into a common schema for training anomaly detection and reasoning models. This release includes:

Dataset Machine Task Episodes Rows Faults
AURSAD UR3e (6-DOF cobot) Screwdriving 4,094 6.2M 5 types
voraus-AD Yu-Cobot (6-DOF) Pick-and-place 2,122 2.3M 12 types
NASA Milling CNC (3-axis) Milling 167 1.5M Tool wear

Total: 6,383 episodes, 10M+ rows

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) - for robots
setpoint_doc         # Depth of cut (mm) - for CNC
setpoint_feed        # Feed rate (mm/rev) - for CNC
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
aux_vibration_*      # Vibration sensors
aux_acoustic_*       # Acoustic emission
ctx_temp_*           # Joint temperatures

Quick Start 

from datasets import load_dataset

# Load AURSAD (robot screwdriving)
ds = load_dataset("Forgis/factorynet-hackathon", data_dir="aursad")
df = ds['train'].to_pandas()
print(f"AURSAD: {df['episode_id'].nunique()} episodes")

# Load voraus-AD (robot pick-and-place)
ds = load_dataset("Forgis/factorynet-hackathon", data_dir="voraus")
df = ds['train'].to_pandas()
print(f"voraus-AD: {df['episode_id'].nunique()} episodes")

# Load NASA Milling (CNC tool wear)
ds = load_dataset("Forgis/factorynet-hackathon", data_dir="nasa_milling")
df = ds['train'].to_pandas()
print(f"NASA Milling: {df['episode_id'].nunique()} episodes")

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 Dataset
normal Normal operation All
stiff_joint Increased joint friction AURSAD
collision Contact with obstacle voraus-AD
grip_failure Gripper malfunction voraus-AD
missing_part Expected part absent AURSAD
tool_wear Progressive degradation NASA Milling

File Structure 

Forgis/factorynet-hackathon/
├── aursad/
│   └── aursad_factorynet.parquet        # 1.5 GB - UR3e time series
├── voraus/
│   └── voraus_ad_100hz_factorynet.parquet  # 461 MB - Yu-Cobot time series
├── nasa_milling/
│   ├── nasa_milling_factorynet.parquet  # 15 MB - CNC time series
│   └── nasa_milling_metadata.json       # Episode metadata with wear labels
├── metadata/
│   ├── aursad_metadata.json             # 78 MB - AURSAD episode metadata
│   └── voraus_metadata.json             # 53 MB - voraus episode metadata
├── schema.json                          # FactoryNet schema reference
├── factorynet_loader.py                 # Easy-load Python utility
└── README.md

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 (NASA Milling)
  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:

License

CC BY 4.0 - Free to use with attribution.

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