Task data
Navigation training data
Navigation training data helps physical AI teams collect scoped examples in homes, offices, sidewalks, warehouses, and logistics routes. When sourcing it, specify egocentric video, IMU, odometry, and scene metadata, target volume, delivery format, rights, consent, and QA rules for route coverage, timestamp sync, obstacle labels, and privacy review.
Quick facts
- Task
- Navigation
- Modality
- egocentric video, IMU, odometry, and scene metadata
- Environment
- homes, offices, sidewalks, warehouses, and logistics routes
- Volume
- 50-300 route traversals with obstacle and recovery labels
- Format
- MCAP, ROS bag, MP4 plus CSV/JSON telemetry
- QA
- route coverage, timestamp sync, obstacle labels, and privacy review
Comparison
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | map-only data misses visual ambiguity, humans, clutter, and recovery behavior |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
What to specify for navigation
The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, MCAP, ROS bag, MP4 plus CSV/JSON telemetry delivery expectations, rights, consent, and what counts as an accepted sample. Registry sources show that task data is only reusable when collection setup and task distribution are explicit [1]. Buyers should also pin delivery expectations to formats and documentation they can validate before scale [2].
Why public data is usually not enough
map-only data misses visual ambiguity, humans, clutter, and recovery behavior. Benchmark and vendor sources show that task labels, rights, and capture context are not interchangeable across deployments [3]. A buyer-specific request lets the team request the exact object set, environment, geography, and QA rubric needed for model training or evaluation.
Navigation buyer scenario
A realistic navigation request starts when a robotics team has a model behavior that fails in homes, offices, sidewalks, warehouses, and logistics routes. The team does not just need more video; it needs examples where route coverage, timestamp sync, obstacle labels, and privacy review can be verified repeatedly [4].
[5]"AI Habitat provides embodied AI datasets and simulation assets for navigation evaluation."
That means the supplier must show the requested egocentric video, IMU, odometry, and scene metadata, prove the capture context, and deliver MCAP, ROS bag, MP4 plus CSV/JSON telemetry in a way the buyer can test before scaling.
Navigation sample acceptance criteria
A useful sample for robot navigation dataset should include at least one accepted episode, one borderline or failed example, a complete metadata manifest, and a note explaining how the supplier would scale only to 50-300 route traversals with obstacle and recovery labels [6]. If the sample cannot show route coverage, timestamp sync, obstacle labels, and privacy review, the buyer should reject it before funding a larger batch.
Navigation task taxonomy and coverage
A robot navigation dataset request should be task-specific, not template-level. Define the sub-tasks, capture viewpoints, object/environment coverage, labels, failure modes, and pilot acceptance rules before asking a supplier to scale. Accepted samples identify routes, waypoints, obstacles, and success/failure outcomes, with held-out route metadata for eval design.
| Planning area | Specify | QA question |
|---|---|---|
| Task phases | start/end boundaries, success, failure, recovery | Can reviewers identify every phase? |
| Sensors | wrist/external/egocentric video, state/action, depth/tactile if needed | Are streams synchronized and loadable? |
| Objects and environment | object family, layout, lighting, clutter, material | Does coverage match deployment? |
| Rights and provenance | license, consent, site permission, source manifest | Can legal/procurement audit the source? |
| Route unit | start, goal, waypoint, route ID | only loose walkthrough video |
| Spatial context | map, obstacle, signage, lighting | route cannot be replayed or scored |
| Failure/recovery | blocked path, reroute, localization loss | only clean paths included |
Navigation accepted and rejected examples
Navigation data needs route semantics, not just forward-facing video. Ask for start/goal IDs, obstacles, reroutes, map or localization context, and failure cases so evaluation can measure generalization instead of memorized walkthroughs. Use licensing/provenance review for human or workplace footage, and route robotics-ready requests through the robot training data marketplace once the pilot schema is clear.
Navigation pilot manifest fields
For Navigation, the pilot manifest should include task phase, environment, object or route class, camera/sensor keys, action/state fields when applicable, timestamps, outcome label, failure reason, reviewer decision, rights/provenance files, and the target delivery format. Accepted samples identify routes, waypoints, obstacles, and success/failure outcomes, with held-out route metadata for eval design. The manifest is the bridge between supplier footage and buyer QA: if a reviewer cannot reproduce why a sample passed or failed, the dataset is not ready for scale-up.
Public datasets as references, not drop-in commercial supply
Public robotics datasets can guide schema and benchmark expectations, but commercial use, embodiment fit, action-state coverage, and consent/provenance are dataset-specific. Treat them as references unless official terms and buyer review support the intended use.
Pilot package before scale-up
Require a small loadable pilot with raw media/logs, manifest, labels, accepted and rejected samples, consent/provenance artifacts, and validation in the target format. Reject missing fields, broken sync, unclear boundaries, unsupported rights, and samples with only clean successes. For warehouse, kitchen, or industrial variants, compare the nearest warehouse, kitchen, or industrial sourcing spec before scaling.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- Project site
AI2-THOR provides interactive embodied AI environments for household navigation and tasks.
ai2thor.allenai.org ↩ - Project site
ScanNet supplies indoor scene data relevant to navigation perception and reconstruction.
scan-net.org ↩ - Dataset page
Waymo Open Dataset provides route and autonomous navigation perception data for mobile agents.
waymo.com ↩ - cloudfactory.com autonomous vehicles
Autonomous vehicle annotation vendors cover perception data workflows for navigation systems.
cloudfactory.com ↩ - Project site
AI Habitat provides embodied AI datasets and simulation assets for navigation evaluation.
aihabitat.org ↩ - NVIDIA: Physical AI Data Factory Blueprint
NVIDIA's physical AI data factory blueprint includes robotics and autonomous vehicle development workflows.
investor.nvidia.com ↩ - truelabel egocentric data glossary
Internal contextual link to the egocentric data definition.
truelabel.ai - truelabel sourcing brief intake
Internal contextual link to Truelabel's sourcing intake workflow.
truelabel.ai - truelabel VLA training data sourcing
Internal contextual link to VLA training data sourcing.
truelabel.ai - truelabel warehouse robotics data sourcing
Internal contextual link to warehouse robotics data sourcing.
truelabel.ai - truelabel kitchen manipulation data sourcing
Internal contextual link to kitchen manipulation data sourcing.
truelabel.ai - truelabel LeRobot format guide
Internal contextual link to the LeRobot format guide.
truelabel.ai - truelabel LeRobot dataset alternative comparison
Internal contextual link to the LeRobot dataset alternative comparison.
truelabel.ai - truelabel eval data for robotics hub
Internal contextual link to robotics eval data sourcing.
truelabel.ai - truelabel teleoperation training-data page
Internal contextual link to teleoperation training data sourcing.
truelabel.ai - truelabel robot demonstrations training-data page
Internal contextual link to robot demonstration training data sourcing.
truelabel.ai - truelabel hand-object interaction data page
Internal contextual link to hand-object interaction training data requirements.
truelabel.ai - truelabel egocentric video datasets hub
Internal contextual link to the egocentric video datasets hub.
truelabel.ai
FAQ
What is robot navigation dataset?
robot navigation dataset refers to data collected for homes, offices, sidewalks, warehouses, and logistics routes. It usually includes egocentric video, IMU, odometry, and scene metadata, metadata, and task outcomes that help train or evaluate physical AI systems.
What should a sourcing request include?
It should include task definition, environment, modality, volume, format, rights, consent, budget, deadline, and QA checks such as route coverage, timestamp sync, obstacle labels, and privacy review.
What format should buyers request?
MCAP, ROS bag, MP4 plus CSV/JSON telemetry is the recommended starting point, but truelabel can route buyer-defined schemas when the training pipeline needs a custom layout.
Can this be exclusive?
Yes. Net-new sourcing requests can request exclusive commercial rights, while off-the-shelf datasets are usually non-exclusive unless the buyer explicitly purchases exclusivity.
What should a robot navigation dataset request include?
Include target task phases, environment and object coverage, sensors/cameras, action/state fields when applicable, labels, success/failure outcomes, privacy/licensing artifacts, delivery format, and pilot acceptance criteria.
Sourcing data for robot navigation dataset
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering robot navigation dataset data with consent artifacts and commercial licensing attached.
Request navigation training data