Manipulation
Hands, tools, object state, contact, precision, bimanual coordination, and recovery.
Application / physical AI
Human Demonstration Data Built Around the Robotics Task.
Design the viewpoint, task structure, modalities, annotations, and variation around what your robotics system needs to learn or evaluate.
Real Tasks.
Observable State Changes.
Structured Delivery.
EGXO designs human demonstration data for physical AI teams that need real interactions, deliberate variation, and formats their training pipeline can use.
One Task, Different Learning Objectives
The viewpoint, labels, sensors, and task variation change with the model and training strategy.
VLA Systems
Visual sequences aligned with goals, instructions, step boundaries, and observable outcomes.
World Models
Long-horizon state transitions, temporal 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 goals.
Evaluation
Controlled variations, negative examples, task completion, failure categories, and robustness.
Human-to-robot bridge
Human Demonstrations Provide Supervision—Not Robot Controls.
Egocentric demonstrations can show task sequence, affordance cues, hand-object contact, and state changes. Exocentric views can preserve body motion and scene geometry.
Robot-native controls may still come from rollouts, teleoperation, simulation, action retargeting, pose estimation, or policy-specific labels. The data plan should define how human demonstrations connect to that training path.
Compare Human Demonstrations and Robot-Native Data ↗Read the Embodied AI Definition ↗Use the Robotics Data Glossary ↗From Use Case to Delivered Dataset
Keep collection, quality review, rights, documentation, and ingest tied to the same project requirements.
- 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.
Custom robotics data
Describe the Task Your System Needs to Learn.
We’ll turn the task, environment, viewpoint, format, and success criteria into a focused pilot.
Review Custom Egocentric Collection ↗Discuss a Robotics Pilot