DataPack
EgoCentric — Household Tasks
The primary deployment environment for humanoid VLA models — first-person household data covering navigation, object interaction, room transitions, and real-world environmental variation.
Solution
VLA architecture has matured. Twelve commercial humanoid platforms are available today. The constraint is no longer the model or the hardware.
It's the structured Physical AI training data.
Pre-packaged DataPacks and custom schemas — engineered to close the gaps your model is actually failing on.
Browse Relevant DataPacks →The Core Problem
VLA models fail in deployment when training data doesn't represent the environments they'll actually operate in. The architecture is ready. The data engine isn't.
Performance Gaps Addressed
Models trained on one factory floor or three university kitchens don't generalize. Coverage across room types, lighting, acoustics, and object categories must be designed in — not assumed from volume.
Third-person data teaches a model to watch a task. Egocentric data teaches it to perform one—first-person perspective isn't a preference — it's a requirement.
Without task-state fields, a model learns frames instead of the state machine it needs to complete long-horizon tasks.
For commercial robotics, industrial automation, and models heading into due diligence, chain-of-title on training data is a deployment requirement — not a legal afterthought.
Relevant DataPacks
Generic pipelines deliver frames. These DataPacks deliver the structured signal VLA and world-model teams need — egocentric perspective, environment coverage, scene context, object-state changes, task-state sequencing, and provenance built for commercial deployment.
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DataPack
The primary deployment environment for humanoid VLA models — first-person household data covering navigation, object interaction, room transitions, and real-world environmental variation.
DataPack
First-person industrial workflows for VLA generalization — material handling, structured logistics, and real-world factory environments that stress-test world model pretraining.
DataPack
Dynamic shared spaces for sim-to-real transfer — human interaction, guest areas, service workflows, and the unpredictable social environments physical AI must generalize across.
DataPack
Task-state and object-state annotation for long-horizon behavior — helping models learn sequences, state changes, temporal continuity, and physical context instead of isolated frames.