Real environments are messy.
Furniture moves. Doors close. People cross paths. Lighting and layouts change without warning.
FIG. 01 PHYSICAL AI / NAVIGATION UNIT
ceptionaiReal-world data for robots that move.
The data layer for indoor navigation, spatial intelligence, and embodied AI.
We turn first-person movement through homes, workplaces, and operational environments into structured training and evaluation data.
DWG. CP-001 · REV A
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( 01 ) THE MISSION
Teach machines how people navigate the real world.
01 / OBSERVE
Robots need more than clean lab demonstrations. They need human examples of turning, waiting, yielding, avoiding, and re-planning in spaces that constantly change.
02 / STRUCTURE
ceptionai pairs real-world movement with synchronized sensor streams, spatial context, and decision-level annotations — ready for model training and evaluation.
( 02 ) THE DATA GAP
Indoor intelligence is blocked by real-world data.
Navigation models fail at the edge cases that define everyday movement. Our datasets focus on those hard, high-value moments.
Raw video lacks decisions.
Pixels alone do not explain why a person stopped, yielded, changed direction, or rejected a route.
Benchmarks miss reality.
Teams need controlled evaluations built from real spaces, real constraints, and repeatable failure modes.
( 03 ) THE CEPTIONAI LOOP
From human movement to model-ready episodes.
A modular pipeline for custom collection, annotation, quality assurance, and secure delivery.
SYSTEM / CP-PIPE-01
One continuous data system.
We scope the environments and behaviors your model needs, then deliver a consistent dataset with traceable provenance.
Scope a collection →Collect in context
First-person video and optional body-worn IMU across representative indoor routes.
Align every signal
Time-aligned sensor streams and route-level metadata create a coherent episode.
Label the decisions
Passability, obstacles, stops, turns, yields, re-plans, and outcome quality.
Validate and package
Human-reviewed, de-identified outputs shaped for training, simulation, or evaluation.
( 04 ) SIGNAL + LABEL PRIMITIVES
Measure what matters for movement.
Combine core sensor streams with the spatial and behavioral signals your robotics stack needs.
Egocentric Vision
EV-01 · SENSORInertial Motion
IM-02 · SENSORRelative Trajectory
RT-03 · SPATIALOccupancy + Map
OM-04 · SPATIALObject Tracking
OT-05 · LABELDynamic Obstacles
DO-06 · LABELDecision Events
DE-07 · BEHAVIOROutcome + Quality
OQ-08 · QA( 05 ) DATA PROGRAMS
Built for the environments your robots enter.
Start with a focused pilot or commission a multi-environment program around your model, robot, and deployment path.
Navigation Foundations
Multi-room routes through homes and offices, with doors, corridors, furniture, clutter, and human traffic.
Explore program →Operational Environments
Routes around racks, carts, pallets, narrow passages, shared work areas, and temporary obstructions.
Explore program →Dynamic Edge Cases
Curated episodes of crossing, yielding, blockage, route rejection, recovery, and re-planning.
Explore program →( 06 ) RESPONSIBLE DATA
Privacy is part of the product.
Every collection program is designed around clear scope, controlled handling, and delivery-ready de-identification.
01Consent-aware collection
Documented participant and venue permissions aligned with the intended use.
Documented participant and venue permissions aligned with the intended use.
02Sensitive-content controls
Faces, screens, documents, addresses, and restricted areas are masked or excluded.
Faces, screens, documents, addresses, and restricted areas are masked or excluded.
03Traceable quality assurance
Episode-level provenance, review status, and collection metadata support audits.
Episode-level provenance, review status, and collection metadata support audits.
04Controlled delivery
Customers receive scoped, de-identified data packages — not unmanaged raw footage.
Customers receive scoped, de-identified data packages — not unmanaged raw footage.
( 07 ) REFERENCE
Frequently asked.
ceptionai provides real-world egocentric navigation datasets, synchronized sensor streams, spatial annotations, and task-specific evaluation sets for embodied AI teams.
Robot foundation model teams, humanoid robotics companies, indoor service robotics teams, autonomy researchers, and simulation platforms.
Yes. Programs can be scoped around target environments, route types, sensor configurations, edge cases, annotation depth, and delivery formats.
We align on a concrete model or evaluation goal, define a small representative collection, deliver a reviewed sample, and use the results to plan scale.
( CONTACT ) CP-001 / REQUEST PILOT
Build your real-world data program.
Tell us what your robot needs to understand. We will help scope the environments, signals, labels, and evaluation criteria for a focused pilot.