Datasets:

Forgis
/

factorynet-hackathon

license: cc-by-4.0
task_categories:
  - time-series-forecasting
  - robotics
  - tabular-classification
tags:
  - robotics
  - manufacturing
  - anomaly-detection
  - physical-ai
  - industrial
  - tool-wear
  - cnc
  - franka-panda
  - assembly
  - manipulation
size_categories:
  - 10M<n<100M
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
RH20T	Franka Panda (7-DOF)	Manipulation	500	1.2M	Normal
REASSEMBLE	Franka Panda (7-DOF)	Assembly	100	1.0M	Task success/fail

Total: 6,983 episodes, 12M+ 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
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")

# Load RH20T (Franka manipulation tasks)
ds = load_dataset("Forgis/factorynet-hackathon", data_dir="rh20t")
df = ds['train'].to_pandas()
print(f"RH20T: {df['episode_id'].nunique()} episodes")

# Load REASSEMBLE (Franka assembly tasks)
ds = load_dataset("Forgis/factorynet-hackathon", data_dir="reassemble")
df = ds['train'].to_pandas()
print(f"REASSEMBLE: {df['episode_id'].nunique()} episodes")

Minimum Viable Episode (MVE)

Most datasets in FactoryNet satisfy 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.

Exception: NASA Milling - This dataset contains only effort signals (spindle current, vibration, acoustic emission) with no dynamic setpoints. Machining parameters (depth of cut, feed rate) are constant per episode and stored in metadata only. For anomaly detection on this dataset, "normal" behavior should be inferred from healthy episodes rather than comparing to commanded setpoints.

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
`task_failure`	Assembly step failed	REASSEMBLE

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 effort signals (current, vibration, acoustic)
│   ├── nasa_milling_extensions.parquet     # Extensions with vibration/acoustic data
│   └── nasa_milling_metadata.json          # Episode metadata with wear labels + machining params
├── rh20t/
│   ├── rh20t_factorynet.parquet            # 31 MB - Franka manipulation
│   └── rh20t_metadata.json                 # Episode metadata
├── reassemble/
│   ├── reassemble_factorynet.parquet       # 406 MB - Franka assembly
│   ├── reassemble_extensions.parquet       # 19 MB - Gripper data
│   └── reassemble_metadata.json            # Episode + segment metadata
├── schema.json                             # FactoryNet schema reference
├── factorynet_loader.py                    # Easy-load Python utility
└── README.md

Use Cases

Anomaly Detection: Train classifiers to detect faulty operations
Fault Diagnosis: Identify which component/joint is failing
Remaining Useful Life: Predict when tool/component will fail (NASA Milling)
Sim2Real Transfer: Use real data to calibrate simulators
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
NASA Milling: NASA Open Data - Public Domain
RH20T: Project Page - Real Human-Robot 20 Tasks
REASSEMBLE: GitHub - Assembly/Disassembly Dataset

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