FactoryBench

FactoryBench is a benchmark for evaluating machine-behavior reasoning in time-series models and LLMs over industrial robotic telemetry. Question-answer pairs are organised along the four levels of Pearl's causal hierarchy:

Level	Capability	Example
L1 — State	Identify the operational state from raw signals	"Which fault, if any, is occurring in this episode?"
L2 — Intervention	Predict the effect of an intervention	"How would the joint torques change if the payload were doubled?"
L3 — Counterfactual	Reason about alternative histories	"Would the collision still have occurred if the speed had been 50% lower?"
L4 — Decision	Engineering decision-making (troubleshooting + optimisation)	"Given this anomaly, what is the most likely root cause and remediation?"

The benchmark is grounded in FactoryWave, a dense multivariate telemetry dataset collected from a UR3 collaborative robot (125 Hz) and a KUKA KR10 industrial arm (83 Hz), supplemented with the AURSAD and voraus-AD open-source datasets.

Dataset summary

• 70,918 Q&A pairs across four causal levels and three splits (train/validation/test).
• 5 answer formats: single-select MCQ, multi-select MCQ, ranking, tensor/numerical, free-form (judged by an LLM-as-judge voting protocol).
• Telemetry from real industrial robots with systematic fault injection (27 atomic mechanisms across pick-and-place, screwing, and peg-in-hole tasks).

Repository layout

FactoryBench/
├── factorybench_qa/                 # Question-answer pairs
│   ├── level_1/{train,validation,test}.jsonl
│   ├── level_2/{train,validation,test}.jsonl
│   ├── level_3/{train,validation,test}.jsonl
│   └── level_4/{train,validation,test}.jsonl
├── knowledge_graph/                 # Combined knowledge graph
│   ├── knowledge_graph.json         # Machines, grippers, tasks, events, faults, anomalies, error→protocol map, relevance specs
│   └── SCHEMA.md                    # Field-level schema documentation
└── factorywave/                     # Underlying telemetry & metadata
    ├── episodes.parquet             # Episode-level metadata (9,728 episodes)
    ├── flow.parquet                 # Task flow definitions
    ├── kuka_signals.parquet         # KUKA KR10 signals (~83 Hz, 1,428 episodes)
    ├── ur_signals.parquet           # UR3 signals (~125 Hz, 3,076 episodes)
    ├── ur_signals_10hz.parquet      # UR3 signals (10 Hz, 3,984 episodes — disjoint from ur_signals)
    └── ur_screwdriver_signals.parquet  # UR3 screwdriver subset (~125 Hz, 1,240 episodes)

Note on UR coverage. ur_signals.parquet (~125 Hz) and ur_signals_10hz.parquet (10 Hz) cover disjoint UR3 episode subsets — no episode appears in both files. Together they span 7,060 distinct UR3 episodes; ur_screwdriver_signals.parquet adds another 1,240 screwdriver-task episodes. Each row in episodes.parquet corresponds to exactly one signal table.

Knowledge graph

knowledge_graph/knowledge_graph.json is the structured "world model" that grounds FactoryBench Q&A items: machine and gripper capability tables, the task/event vocabulary, the fault root-cause catalogue, the observable-anomaly catalogue, the error→protocol mapping used to derive ground-truth answers for L4 troubleshooting items, an anomaly severity ranking, and the relevance specs that drive fault-aware sub-series sampling. See knowledge_graph/SCHEMA.md for field-level documentation.

import json, urllib.request
url = "https://huggingface.co/datasets/FactoryBench/FactoryBench/resolve/main/knowledge_graph/knowledge_graph.json"
kg = json.loads(urllib.request.urlopen(url).read())

# Machine spec lookup
machines_by_id = {m["machine_id"]: m for m in kg["machines"]}

# Error → operator protocol (the ground truth for L4 troubleshooting answers)
protocol_for = {e["root_cause"]: e["ur3_protocol"] for e in kg["root_cause_error_mapping"]}
print(protocol_for["collision_rigid_object"])

Q&A pair counts

Level	Train	Val	Test	Total
L1	12,674	1,338	1,309	15,321
L2	33,311	3,428	3,487	40,226
L3	2,353	265	321	2,939
L4	9,949	1,251	1,232	12,432
Total	58,287	6,282	6,349	70,918

Q&A fields

Each line in factorybench_qa/level_*/*.jsonl is a single Q&A item:

Field	Description
id	Unique item identifier
level	Causal level (1–4)
template_id	Question template the item was generated from
template_type	Answer format (single_choice, multi_choice, ranking, tensor, free_form)
hides	Channels/fields hidden from the model in this item
question	Natural-language question
options	Answer options (for MCQ/ranking templates)
answer	Ground-truth answer
root_cause	Underlying fault/cause (Level 4 only)
acceptance_bounds	Tolerance for numerical answers
provenance	Source episode(s) and channels used to derive the item
context	Time-series and metadata context exposed to the model

Loading the data

from datasets import load_dataset

# Load a single level/split
ds = load_dataset(
    "FactoryBench/FactoryBench",
    data_files="factorybench_qa/level_1/test.jsonl",
    split="train",
)

# Load underlying telemetry
import pandas as pd
root = "hf://datasets/FactoryBench/FactoryBench/factorywave"
episodes = pd.read_parquet(f"{root}/episodes.parquet")
ur_125 = pd.read_parquet(f"{root}/ur_signals.parquet")
ur_10  = pd.read_parquet(f"{root}/ur_signals_10hz.parquet")  # different episodes
kuka   = pd.read_parquet(f"{root}/kuka_signals.parquet")
screw  = pd.read_parquet(f"{root}/ur_screwdriver_signals.parquet")

# Load the combined knowledge graph (machines, faults, error→protocol, ...)
import json, urllib.request
kg = json.loads(urllib.request.urlopen(
    "https://huggingface.co/datasets/FactoryBench/FactoryBench/resolve/main/knowledge_graph/knowledge_graph.json"
).read())

Citation

If you use FactoryBench, please cite the dataset and the two upstream open-source datasets it incorporates (AURSAD and voraus-AD):

@misc{anonymous2026factorybench,
  title  = {FactoryBench: Evaluating Industrial Machine Understanding},
  author = {Anonymous},
  year   = {2026},
  note   = {Submission under double-blind review}
}

@article{leporowski2022aursad,
  title   = {{AURSAD}: Universal Robot Screwdriving Anomaly Detection Dataset},
  author  = {Leporowski, B{\l}a{\.z}ej and Tola, Daniella and Hansen, Christian and Iosifidis, Alexandros},
  journal = {arXiv preprint arXiv:2202.03211},
  year    = {2022}
}

@misc{brockmann2024vorausad,
  title         = {voraus-{AD}: A New Dataset for Anomaly Detection in Robot Applications},
  author        = {Brockmann, Jan Thie{\ss} and Rudolph, Marco and Rosenhahn, Bodo and Wandt, Bastian},
  year          = {2024},
  eprint        = {2311.04153},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO}
}

Intended use & limitations

Intended use. Benchmark evaluation of LLMs and time-series models on structured industrial Q&A reasoning tasks (state, intervention, counterfactual, decision-making).

Limitations. Domain-specific to factory and industrial robotic scenarios; may not generalise to open-domain Q&A. Faults are atomic and drawn from a closed catalogue of 27 physically injected mechanisms — different from compound or gradual real-world faults. The dataset shows a size imbalance between Levels 2 and 3.

Out-of-scope. Not intended for deployment in safety-critical, medical, legal, or financial decision systems without further validation by domain experts.

License

Released under the MIT License.