knowledge_graph/SCHEMA.md

FactoryBench Knowledge Graph — Schema

knowledge_graph.json bundles the structured "world model" that grounds FactoryBench Q&A items: machine and gripper capabilities, the task/event vocabulary, the fault catalogue, and the operator-facing remediation protocols. The prompt builders and answer-derivation code consult these tables to (a) inject relevant context into prompts and (b) generate ground-truth answers for L4 troubleshooting items.

The file is a single JSON object with a top-level $schema_version and a description, plus the ten sections below. Every section value is either a list of records or a dict.

machines (list)

Robot arms used as test platforms. One entry per physical machine class.

Field	Type	Notes
machine_id	int	Stable id referenced by datasets[*].machine_id.
machine_model	str	Vendor model code (e.g. "UR3e").
manufacturer, series	str	Vendor metadata.
machine_type	str	E.g. "collaborative robot".
weight_kg, payload_kg, degrees_of_freedom	num	Physical specs.
joint_rotation	list[num]	Per-joint rotation limits.
typical_applications	list[str]	Free-text capability tags.
control_interfaces	list[str]	E.g. ["UrScript", "PolyScope"].
joint_speed_limits, rated_current_per_joint	list[num]	Per-joint maxima.
safety_modes, joint_modes, robot_modes, runtime_states	list[obj]	Enum tables (id ↔ human-readable label).
used_in_paper_for	list[str]	Datasets/tasks this machine appears in.

grippers (list)

End-effectors. One entry per gripper class.

Field	Type	Notes
gripper_id	int	Stable id referenced by datasets[*].gripper_id.
gripper_model, manufacturer, gripper_type, actuation	str	Vendor metadata.
finger_count, weight_kg, payload_kg, payload_kg_form_fit	num	Physical specs.
grip_force_range_N, torque_range_Nm, max_stroke_mm, opening_range_mm	obj/list	Force/motion ranges.

datasets (list)

The four episode collections that make up FactoryWave. One entry per (machine, gripper, task) combination.

Field	Type	Notes
dataset_id	str	E.g. "factorywave", "aursad", "vorausad".
name, description	str	Human-facing label.
machine_id, gripper_id	int	FK into machines / grippers.
task_id	int	FK into tasks.
source	str	URL or citation.
license	str

tasks (list)

Task-level vocabulary: each task is a sequence of named phases the robot moves through.

Field	Type	Notes
id	int	Stable id referenced by datasets[*].task_id and used in relevance_specs.
name, description	str	E.g. "pick_and_place".
phases	list[obj]	Ordered phase descriptors (id, name, intent). Used by phase-gated relevance sampling.

events (list)

Atomic events that can occur during a task (e.g. collision triggers, gripper transitions, screwdriver phase changes).

Field	Type	Notes
id	int	Stable event id encoded in episode rows under event.
name, description	str
tasks	list[int]	Tasks where this event can occur.
variables	list[obj]	Per-event observable variables and their ranges.

root_causes (list)

Catalogue of injectable fault mechanisms. The "physics" side of a fault.

Field	Type	Notes
fault_id	int	Stable id used in episode-level fault_label. Also the FK from root_cause_error_mapping.
task	str	Which task this fault applies to.
root_cause	str	Snake-case identifier (e.g. "collision_rigid_object"). FK from root_cause_error_mapping[*].root_cause.
description	str	Plain-language explanation.
severity_levels	list[obj]	Mild / moderate / severe variants and their parameters.
injectable	bool	Whether the fault was actively injected (vs. passively observed).
possible_anomalies	list[str]	FK into anomalies[*].anomaly_name.
simulation_procedure	str	How to reproduce the fault.
datasets	list[str]	FK into datasets[*].dataset_id.

anomalies (list)

Catalogue of observable symptoms (the "phenomenology" side of a fault).

Field	Type	Notes
anomaly_name	str	Snake-case identifier (e.g. "sudden_torque_spike").
description	str	What the anomaly looks like in the data.
relevant_features_from_schema	list[str]	Which channels (feedback_speed_*, effort_target_torque_*, etc.) the anomaly manifests on.

A single fault can manifest as multiple anomalies, and a single anomaly can be caused by multiple faults — the join is via root_causes[*].possible_anomalies.

root_cause_error_mapping (list)

The error-to-protocol table. Maps each fault_id / root_cause to the UR3 controller error it raises (when any) and the operator-facing remediation protocol.

Field	Type	Notes
fault_id	int	FK into root_causes.
root_cause	str	Mirror of root_causes[*].root_cause (denormalised for direct lookup).
ur3_error_code	str / null	UR3 controller error code (e.g. "C 39 A 1"), null for software-only faults.
ur3_error_name	str / null	Human-readable error name as shown on the teach pendant.
ur3_description	str / null	What the controller reports to the operator.
ur3_protocol	str	Step-by-step remediation procedure. This is the ground-truth answer for L4 troubleshooting items.

anomaly_ranking (dict)

Severity ordering of anomalies, used by the rubric scorer.

Field	Type	Notes
ranking_least_to_most_severe	list[str]	Anomaly names in ascending severity.

relevance_specs (dict)

Drives fault-aware sub-series sampling — i.e. picking a window where the fault's signature is observable rather than uniformly random. See src/question_generation/utils/relevance.py.

Field	Type	Notes
version	str	Spec version.
description	str	Human-facing summary.
locality_definitions	dict	The four sampling localities: global (uniform), event (window must contain transient), phase_gated (window must overlap target task phase), cumulative (length-based with optional phase gate).
defaults	dict	Per-locality default parameters merged into each spec.
specs	dict	Per-fault_id overrides. Keys are stringified fault ids; values declare locality, target phases, min window length, etc.

Set FB_RELEVANCE=0 in the environment to bypass and fall back to uniform sampling.

Loading

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 by id
machines_by_id = {m["machine_id"]: m for m in kg["machines"]}

# Error→protocol lookup
protocol_for = {e["root_cause"]: e["ur3_protocol"] for e in kg["root_cause_error_mapping"]}
print(protocol_for["collision_rigid_object"])