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 from the sample 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.
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 ↩
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.
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