Use
Training, evaluation, replay or algorithm validation and target reader.
ROBOT DATA COLLECTION · EMBODIED AI
Robot data collection starts with usable data, not hours of recording
For embodied AI, navigation, manipulation, imitation learning, perception and robot evaluation. Define the training or evaluation need before selecting robots, sensors, scenes, operators and protocols.
SignalsVision, depth, LiDAR, state, action, force/touch and events
QualitySync, calibration, coverage, failures, readability and lineage
First gatePilot capture, replay and training/evaluation pipeline
Inputs needed before scope and quotation
Incomplete material is acceptable; missing inputs will become a validation list.
Task
Objects, initial state, success/failure, variation, repetition and termination.
Signals
Sensors, rates, frames, sync, calibration, encoding and units.
Compliance
Site, people/object rights, privacy, transfer, retention, access and deletion.
Field and system evidence
These materials expose operating variables; they do not prove a universal outcome.
Decision and delivery framework
Derive the schema from downstream use
Imitation, perception, policy evaluation and fault analysis need different signals, rates and labels. Agree on one readable sample across algorithm, robot and data teams.
Separate required, optional and prohibited fields, with units, frames, timestamps, compression and missing-value rules.
- Training/evaluation objective
- Episode and event definition
- Schema, units and frames
- Reader and version
Use a pilot to expose sync and calibration
Capture and replay a small batch before scaling people and sites. Align cameras, depth, LiDAR, joints, actions, force and events on one timebase.
Calibration, drift, drops and resets can invalidate visually complete data; retain raw logs and quality marks.
- Hardware/software release
- Time and drops
- Calibration and transforms
- Reset and termination
Design success, failure and coverage
Success-only demonstrations create bias. Cover objects, positions, lighting, backgrounds, operators and failure classes while controlling variables.
Teleoperation data should record controller, latency, takeover, operator and difficulty; clock time is not valid-data time.
- Scene/object strata
- Success/failure/abort
- Operator and control
- Coverage plan
Deliver through quality gates and a data card
Automatically inspect format, frames, time, range and missing values, then manually review semantics and labels. Trace issues to equipment, site, operator and capture release.
Deliver data, schema, reader, versions, quality report, known limits and authorization. One model result is evidence, not a general guarantee.
- Automated and human QA
- Quarantine and recapture
- Data card and changes
- Rights, retention and deletion
| Gate | Check | Evidence | Failure action |
|---|---|---|---|
| Readable | Files, schema, encoding and units | Validator and reader | Quarantine and export again |
| Aligned | Time, frames, coordinates and calibration | Sync statistics and replay | Calibrate or recapture |
| Semantic | Task, events and success/failure | Sampling and second review | Relabel or add capture |
| Compliant | Rights, privacy, scope and retention | Data card and access record | Delete, redact or stop |
Evidence sources
Use the exact manufacturer edition, interface documentation, representative tests and the written project baseline. Internal catalogs help discovery but do not replace current manufacturer evidence.
Limits and exclusions
- No specification is promised before exact device rights, channels and rates are verified.
- Hours, episodes and bytes alone do not measure usable data or model effect.
- People, speech, commercial sites and sensitive production data require authorization and review.
- A dataset cannot guarantee model metrics; task, distribution, algorithm and evaluation matter.
Capabilities, price and lead time are conditional planning information, not a performance or return guarantee. The written configuration, test and contract control the final scope.
Frequently asked questions
How is robot data collection priced?
By equipment, task, site, operator, duration, valid rate, labels, quality and format. A pilot is the safer estimate.
Can you collect teleoperation data?
Where the exact device, site and safety conditions allow it, with control, rate, latency, state/action, takeover and failure definitions.
How do we know the data can train a model?
Run a pilot through reading, replay and the target training/evaluation path; inspect sync, calibration, distribution, labels and rights.
Can you guarantee model improvement?
No. Validate on a fixed baseline and evaluation set with algorithm, release and experiment conditions recorded.
Related decisions
Start with the job, evidence and boundary
Share the site, task, exact robot if known, target date, interfaces and unacceptable failures. We will return missing inputs and validation gates.