— Use Case · Navigation & Scene Understanding
Help robots read the spaces they move through.
Before a robot can act in a space, it has to understand it: layout, obstacles, people, and the way scenes change. dexset builds navigation and scene-understanding datasets from the environments where your robot will operate.
Scene capture · Retail aisle
— Capabilities
What navigation datasets capture.
Indoor route capture
Traversals through warehouses, stores, facilities, and homes with route and waypoint metadata.
Obstacle interaction
Static and dynamic obstacles — carts, people, spills, clutter — encountered and navigated in real time.
Spatial layout metadata
Floor plans, zone tags, and landmark annotations that ground video in spatial structure.
Scene segmentation
Pixel-level labels for floors, walls, fixtures, people, and traversable space.
Lighting and clutter variation
The same routes captured across conditions, shifts, and seasons for robust coverage.
Temporal context
Sequential labels that capture how scenes evolve — doors open, people move, layouts change.
— In Scope
Environments covered.
— Compliance & Trust
Compliant by design, across every dataset.
From first consent form to final delivery, dexset workflows are built to meet data protection, security, and labor standards across the regions where we capture and the regions where our customers operate.
Informed consent
Every participant is briefed and signs a release before capture begins. Consent records attach to each clip, and withdrawal requests are honored across all delivered versions.
Privacy protection
Face blurring, anonymization, and exclusion zones are applied wherever required. We minimize personal data by design and never collect more than the task brief demands.
Data security
Footage is encrypted in transit and at rest, with role-based access controls, signed delivery URLs, and per-recipient transfer records on every dataset.
Regional compliance
Capture and delivery workflows are designed to align with GDPR and UK GDPR, CCPA/CPRA, PIPL, APPI, and LGPD requirements, with regional data-residency options where needed.
Traceability & audit
Source, consent, capture, environment, and annotation records persist for every dataset, supporting audits that run from raw footage to the exported training file.
Responsible sourcing
Capture operators, demonstrators, and annotators are fairly paid and work under documented, safe conditions — quality data should never come from exploitative labor.
— What Teams Say
Trusted by robotics teams.
★★★★★
“The egocentric hand-pose labels are the best we have evaluated. Our grasp success rate improved 18% after one fine-tuning round.”
Priya Raman
Manipulation Lead, AI Robotics Company
★★★★★
“Coverage scoring meant we knew exactly what variation we were missing before training, not after burning a GPU budget on it.”
Sofia Almeida
Perception Engineer, AI Robotics Company
★★★★★
“We sent one task brief and got back a dataset that loaded into our pipeline on the first try. Custom schema, zero rework.”
Marcus Chen
ML Infrastructure Lead, Independent Robotics Vendor
— FAQ
Frequently asked questions.
What navigation data do you collect?
Route traversals through real facilities with waypoint metadata, static and dynamic obstacle encounters, scene segmentation labels, spatial layout annotations, and repeated passes across lighting and clutter conditions.
Which environments are covered?
Warehouses, retail floors, offices, residential interiors, clinical corridors, industrial facilities, and mixed pedestrian spaces — matched to where your robot will actually move.
How do you label scenes?
Pixel-level segmentation for floors, walls, fixtures, people, and traversable space, plus zone tags and landmark annotations that ground each frame in the facility’s spatial structure.
Can you capture how scenes change over time?
Yes. Sequential labels track doors opening, people moving, and layouts shifting across passes and shifts, giving models the temporal context static maps cannot provide.
What space does your robot move through?
We capture routes and scenes from environments matched to your deployment.