FactoryNet

A multi-embodiment industrial time-series corpus: 56,591 end-to-end task executions (14,847 real, 41,744 simulated), 113M logged timesteps, 7 embodiments, 4 tasks, 27 annotated anomaly types with healthy baselines and counterfactual pairs.

Every signal from every source is mapped into one control-theoretic schema — Setpoint, Effort, Feedback, Context (S-E-F-C) — so a single dataloader works across a 6-DOF arm and a 4-axis CNC gantry, and commanded versus realized dynamics are readable as explicit prediction residuals rather than opaque reconstruction scores.

This repository is anonymized for double-blind review.

Quick start

from datasets import load_dataset

ds = load_dataset("FactoryNet4/factorynet", split="train", streaming=True)
print(next(iter(ds)))

Or read the Parquet directly, which is usually what you want for time series. cnc_000.parquet is 1.1 MB, so this is a fast first look:

import pandas as pd
from huggingface_hub import hf_hub_download

p = hf_hub_download("FactoryNet4/factorynet", "data/cnc_000.parquet",
                    repo_type="dataset")
df = pd.read_parquet(p)

# the same four prefixes on every machine, without vendor-specific names
S = [c for c in df.columns if c.startswith("setpoint_")]
E = [c for c in df.columns if c.startswith("effort_")]
F = [c for c in df.columns if c.startswith("feedback_")]
C = [c for c in df.columns if c.startswith("ctx_")]

The same five lines work unchanged on a 6-DOF arm. The consolidated real UR recording, data/factorywave_ur_consolidated.parquet, is 992 MB with 7,129,261 rows and 125 columns (44 Setpoint, 19 Effort, 25 Feedback, 30 Context), so pass columns=[...] to read_parquet rather than loading it whole.

What is where

Prefix in data/	Source	Files
factorywave_*	our laboratory UR3 and KUKA KR10 recordings, incl. screwdriver telemetry and episode metadata	6
voraus_*	voraus-AD	60
aursad_*	AURSAD	32
cnc_*	UMich CNC milling	1
simulations_baseline_*	simulated nominal episodes	11
simulations_counterfactual_*	matched simulated counterfactuals	10

data/ is 13.7 GB of Parquet. The repository also carries the paired sim-to-real gap artifacts used for the validation campaign: real_csv/ and sim_csv/ (episodes paired by filename), pick_configs/ (per-episode simulation parameters), and gap_reports/ + summary/ (per-episode and aggregated gap analysis).

The S-E-F-C schema

One wide table per source: one row per control tick, joined to an episode table by episode_id. The column prefix carries the role; the suffix _i is the positional joint index 0..5 (base → wrist), so the same column means the same slot on every arm. A column a source does not expose is written as null rather than dropped, so all sources share one schema.

Role	Prefix	What it is	Examples
Setpoint	setpoint_*	commanded intent	target joint position, velocity, acceleration; target TCP pose; gripper_command
Effort	effort_*	actuation energy expended	motor current, joint torque, commanded torque, estimated contact force
Feedback	feedback_*	measured physical outcome	encoder position and velocity, TCP pose, tool accelerometer
Context	ctx_*	environment and static state	payload mass, task phase, fault labels, safety/robot mode, speed scaling, I/O bits

This separation is the point of the corpus. Existing industrial datasets log sensor outcomes without distinguishing what the controller asked for from what the machine did, which makes cross-machine dynamics learning hard to set up at all.

Composition (the corpus as reported in the paper)

Source	Machine	Tasks	Faults	Episodes	Timesteps
Lab (real)	UR3	P&P, Screw, Peg	yes	8,863	11M
Lab (real)	KUKA KR10	P&P	yes	1,799	4M
Open (real)	voraus-AD (Yu-Cobot)	P&P	yes	2,122	16M
Open (real)	AURSAD (UR3e)	Screw	yes	2,045	3M
Open (real)	UMich CNC	Machining	yes	18	18K
Synthetic	Isaac Sim — UR3, UR5, UR10, UR30, KR10	P&P	yes	41,744	79M
Total				56,591	113M

1,553 real counterfactual episodes accompany the faulty runs. Of the 10,662 lab episodes, ~28% are healthy and ~72% contain an injected fault.

Synthetic track — read this before comparing against the paper

This snapshot carries the earlier Isaac Sim 4.5.0 synthetic campaign (simulations_baseline_* and simulations_counterfactual_*), not the five-arm 41,744-episode campaign the paper reports. That campaign is Isaac Sim 5.1 / Isaac Lab 2.3 across UR3, UR5, UR10, UR30 and KR10, and is being uploaded during the review period. Every real subset here is final and matches the paper.

The 5.1 campaign runs GPU-batched PhysX at 500 Hz, with control and logging at 125 Hz for the UR arms and 83.3 Hz for the KR10 (the KRC interpolation cycle), no resampling. Six fault classes: payload addition, motor miscommutation, gripper activation failure, gripper release, collision, path obstacle.

Every episode passes a two-stage validity gate — structural (plausible length, complete phases, part placed, injected event present, non-zero contact force) and trace (every fault must leave a measurable physical effect). 41,744 of 50,000 generated episodes pass. This guarantees that a fault label corresponds to a real physical effect, but it also biases the synthetic faults toward more detectable instances, and acceptance rates differ by fault class. UR3 and KR10 motion profiles are fitted to real recordings; UR5, UR10 and UR30 have no real counterpart and are scaled from the UR3 fit.

Known limitations

Stated here because they determine what the corpus can be used for:

• UR5, UR10 and UR30 exist only in simulation. Any multi-arm result on them is sim-to-sim.
• The validity gate favours detectable faults, so synthetic fault difficulty is not representative.
• Recording context partly predicts fault labels on both real robots (program, day, speed override), so real fault evaluation should stay within a single recording session.
• Simulated motor current is not calibrated to the real robots out of the box. Task and cycle structure transfer from simulation; fault signatures largely do not.
• The KUKA KR10 (KSS 8.3) does not expose joint velocities, commanded TCP pose, or TCP force/torque over RSI; those channels are null.

Licensing

Novel laboratory and synthetic data: MIT. Adapted open-source subsets (voraus-AD, AURSAD, UMich CNC) retain their original licenses and are redistributed under them. Cite the original sources when using those subsets.

Code

Adapters, simulation configs, the full transfer study and every result file behind the paper's figures are in the anonymous code repository linked from the paper. A verification script there re-resolves all 126 plotted values against the raw result files with no data download required.