Warehouse picking dataset bounty
Commission consented robot or egocentric data for SKU picking, bin picking, packing, and shelf workflows.
BOUNTY TEMPLATES
Physical AI data bounty templates
Start with a structured buyer spec instead of a vague vendor brief. Each template defines modalities, capture requirements, QA criteria, metadata, risk flags, budget, and timeline.
DIRECT ANSWER
A physical AI bounty template converts a model data gap into a vendor-ready scope with verifiable acceptance criteria and a clean path into escrowed collection.
Home manipulation dataset bounty
Collect physical AI data for household object handling, door opening, tidying, kitchen, and laundry tasks.
Humanoid teleoperation dataset bounty
Source bimanual or whole-body teleoperation data for humanoid manipulation, mobility, and dexterous workflows.
Robot foundation model eval bounty
Create a compact, rights-cleared evaluation bundle for comparing VLA or robot foundation models on target tasks.
TEMPLATE RESEARCH PATHS
Use templates with datasets, tools, and specs
A bounty template should not be a standalone prompt. It should connect to source research, expected formats, budget estimation, baseline specs, and provider choices so the buyer can defend every field in the eventual request.
Before a team posts a template, it should compare public datasets, decide which fields are truly required, and specify how samples will be accepted or rejected. That turns the template from generic procurement copy into an auditable data contract.
The links below keep each template connected to the operational surfaces that make it useful: source discovery, cost estimation, format requirements, glossary definitions, and marketplace routing.
INTERNAL LINKS
Continue the buyer workflow
Data spec generator
Turn the template into a structured intake with modality, task, geography, rights, metadata, and QA fields.
Robotics data cost estimator
Stress-test the planned hours, geography, exclusivity, and QA strictness before asking suppliers for quotes.
Dataset catalog
Use public datasets to calibrate what already exists, what is missing, and where custom data must be cleaner.
Format guides
Convert the template from plain English into file, schema, manifest, and validation requirements.
Specs and FAQ
Use the shared capture, delivery, consent, and quality baseline before writing template-specific overrides.
Training data task hub
Map the bounty to the model behavior it should improve instead of only naming a data modality.
Vendor alternatives
Decide whether the template needs a marketplace bounty, a managed service, or a specialist data partner.
EXTERNAL REFERENCES
Source context to verify
Scale AI physical AI data engine
Market context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
LeRobot documentation
Robotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
Open X-Embodiment
A cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
DROID dataset
A large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.