Manipulation
Hands, tools, object state, contact, precision, bimanual coordination, and recovery.
Application / physical AI
Robotics Training Data Should Begin With the Failure You Need to Fix.
Capture viewpoint, task structure, modalities, annotations, and variation around a concrete learning objective—not a generic promise of more video.
Demonstrations.
State Transitions.
Evaluation Evidence.
EGXO scopes human behavior data for physical AI teams that need real tasks, observable interactions, and delivery structures their training pipeline can ingest.
Training Objectives Change the Collection
The same task can require completely different data depending on what the model must learn.
VLA Systems
Visual sequences aligned with goals, instructions, step boundaries, and observable outcomes.
World Models
Long-horizon state transitions, causal action context, interruptions, and environment variation.
Navigation
Continuous scene context, route decisions, obstacles, interactions, and localization signals.
Humanoids
Whole-body motion, balance, reach, locomotion, workspace geometry, and task intent.
Evaluation
Controlled variations, negative examples, task completion, failure categories, and robustness.
Human-to-robot bridge
Human Video Is Supervision—Not a Robot Action Space.
Egocentric demonstrations can expose intent, sequence, affordances, hand-object contact, and state changes. Exocentric views can preserve body motion and scene geometry. Neither removes the embodiment gap between human and robot.
Training plans may still require robot rollouts, teleoperation, simulation, action retargeting, pose estimation, or policy-specific labeling. The collection brief should identify that bridge before promising a delivery format.
From Brief to Buyer-Ready Release
A clean workflow keeps collection, QA, governance, and ingest tied to the same acceptance criteria.
- 01
Specify
Model objective, tasks, views, environments, modalities, rights, and success criteria.
- 02
Pilot
Stress-test camera placement, instructions, metadata, privacy, and hard task variation.
- 03
Capture
Run versioned protocols with observable task boundaries and traceable session context.
- 04
Validate
Combine file, metadata, visibility, annotation, privacy, and acceptance checks.
- 05
Deliver
Package documented releases and prove them in the target loader before acceptance.
Project brief
Bring the Model Failure, Not a Vague Hour Count.
We’ll use it to define the smallest useful pilot and the evidence required to scale.
Scope Robotics Data