Budget
Use this as a planning range before vendor quotes and sample review.
BOUNTY TEMPLATE
Home manipulation dataset bounty
Collect physical AI data for household object handling, door opening, tidying, kitchen, and laundry tasks.
DIRECT ANSWER
This template is designed for home-manipulation buyers who need vendors to quote against specific capture, metadata, QA, rights, and delivery requirements.
Timeline
Assumes scoped capture, sample QA, and buyer-hosted delivery.
Tags
Capture requirements
- 120-170 degree FOV
- room labels
- object state before/after
- contributor consent
QA criteria
- privacy review passed
- hands in frame
- realistic home clutter
- task completion labels
Metadata fields
- room type
- object class
- task outcome
- contributor ID
- consent artifact ID
Risk flags
- private-home privacy
- minors or bystanders
- exclusive rights
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
Bounty templates hub
Compare adjacent bounty patterns before committing to one capture plan, review cadence, or budget range.
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.
USE THIS TEMPLATE
Turn it into a request.
Use this scope as the starting point for a sourcing brief our team can review.