Objective
The first section should make the model behavior, target embodiment, target environment, and modality package precise enough for a supplier to quote against.
FREE TOOL
Physical AI data spec generator
Convert a data gap into a buyer-ready bounty spec with capture scope, target robot, environment, rights route, metadata fields, QA requirements, delivery format, holdout split, and milestones.
DIRECT ANSWER
A good bounty spec turns vague data needs into verifiable acceptance criteria: modality, task, location, volume, rights, metadata, QA, and delivery path.
Spec presets
Generated bounty spec
Warehouse Picking physical AI data bounty
Warehouse Picking physical AI data bounty: 120 accepted hours for mobile manipulator with gripper in mixed-SKU warehouse aisles and packing stations, delivered as LEROBOT with exclusive net-new commercial training and evaluation rights.
Objective
- Collect 120 accepted hours or equivalent episodes for warehouse picking.
- Target robot or embodiment: mobile manipulator with gripper.
- Target environment: mixed-SKU warehouse aisles and packing stations.
- Primary modality package: teleoperation + RGB-D + action/state.
Capture requirements
- Capture in US warehouses with enough site diversity to expose real deployment variance.
- Include accepted examples, rejected examples, edge cases, and negative cases.
- Keep task boundaries, operator instructions, camera or robot setup, and environment notes attached to each session.
- Reserve 15% of accepted data as a target-domain holdout set that is never used for training.
Rights and consent
- Rights route: exclusive net-new commercial training and evaluation rights.
- signed contributor, operator, or site consent artifacts are required for every accepted sample
- Delivery must include source, collection date, reviewer, approved use route, and unresolved restrictions.
QA and acceptance
- Strict QA requires sample review before full collection unlocks.
- Each rejected sample needs a specific reason: missing field, bad sync, wrong task, unclear rights, poor visibility, or deployment mismatch.
- Buyer accepts only samples that parse into the requested schema and match the target task definition.
Delivery
- Preferred delivery format: LEROBOT.
- Delivery package includes raw files when available, normalized manifest, validation output, checksums, rejected-sample log, and rights packet.
- Supplier must provide a small pilot packet before scale: 10-25 accepted samples plus representative rejected examples.
Required metadata fields
sample_idsession_idtask_labelenvironment_typegeographyrobot_or_capture_rigsensor_modalitiesoperator_or_contributor_idconsent_artifact_idlicense_or_terms_idqa_statusrejection_reasonholdout_splitdelivery_format_version
Acceptance criteria
- Sample parses with no missing required fields.
- Task, object set, environment, and modality match the bounty objective.
- Rights and consent artifacts are attached or explicitly marked not applicable.
- Timestamp, action, observation, and metadata alignment pass validation.
- Rejected examples include reproducible reasons and supplier correction notes.
Milestones
- Day 0-3Bounty lock
Final spec, rights route, sample manifest, validation checklist, and reviewer owners.
- Week 1Pilot packet
10-25 accepted samples, rejected samples, source notes, and parser output.
- Week 2+Scale batch
Rolling accepted batches with QA log, consent map, and delivery manifest.
- Final weekFinal review
Accepted dataset, holdout split, rights packet, checksums, and unresolved-risk memo.
METHODOLOGY
What a generated bounty spec should prove
This generator turns a vague data gap into a supplier-readable scope. It names the use case, target robot, environment, modality package, geography, accepted-hour goal, holdout split, delivery format, rights route, consent requirement, QA strictness, and metadata fields.
The output is meant to be edited into a real buyer memo or bounty post. It is not a contract and it is not enough by itself: the buyer still needs sample data, acceptance rules, owner sign-off, and a delivery validation path.
A strong spec should make bad submissions easy to reject. If a supplier cannot produce a pilot packet that parses, matches the task, carries consent and rights evidence, and includes rejected-sample reasons, the scope is not ready to scale.
INTERPRETATION RULES
How to read the result
Acceptance criteria
Acceptance rules should be verifiable through sample parsing, metadata checks, timestamp and action alignment, rights artifacts, and rejection reasons.
Milestones
Milestones protect the buyer from scaling too early: lock scope, review pilot data, scale accepted batches, then run final delivery and rights review.
CALIBRATION SOURCES
References behind the rubric
LeRobot documentation
Format and tooling context for turning a bounty spec into loader-ready robotics data requirements.
Propel Robotics data collection
Provider reference for real-world collection workflows, QA-verified data, and buyer-facing delivery formats.
Traceplane robotics QA
QA reference for why specs should name schema, metadata, integrity, and trajectory-quality checks before data reaches training.
TOOL FOLLOW-UP
Every tool output should route to evidence
A calculator or checker is useful only when it changes the buyer's next step. The output should send the user toward dataset research, rights review, format requirements, budget planning, or a bounty spec with concrete acceptance criteria.
The links below make that workflow explicit and keep tool pages from becoming isolated utilities — opening paths into deeper catalog, template, briefing, and provider research.
External references are included because tool outputs need calibration against the wider robotics data ecosystem. Buyers should be able to compare truelabel's workflow assumptions with public robotics datasets, developer tooling, and market signals.
Use the tool result as a draft memo, not a final answer. A buyer still needs a source link, a sample packet, a rights note, and a concrete acceptance rule before the output becomes a procurement decision. The links below are the evidence trail for that memo.
Where to go next
- Physical AI data toolsMove between cost estimation, dataset fit, license triage, and bounty-spec drafting from one workflow surface.
- Dataset catalogGround tool outputs in real dataset profiles before deciding whether public data or custom collection is the next step.
- Bounty templatesConvert calculator outputs into reusable scopes with capture requirements, QA gates, risk flags, and metadata fields.
- Data briefingsCheck whether licensing, dataset release, or teleoperation news changes the assumptions behind a tool result.
- Robot data formatsTranslate an output into loader, timestamp, manifest, and file-format requirements before sourcing data.
- Physical AI glossaryResolve vocabulary before turning a form result into procurement language a supplier can quote against.
- Physical AI data marketplaceUse truelabel when the result points to a scoped custom collection, dataset supplement, or evaluation package.
- Data annotation companiesCompare where tooling ends and managed labeling, curation, capture, or marketplace sourcing should begin.
Other places to verify the claims
- Scale AI physical AI data engineMarket context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
- LeRobot documentationRobotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
- Open X-EmbodimentA cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
- DROID datasetA large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.