Physical AI
Robots that remember, not just react.
Most robots can only find things by looking again — scanning the room from scratch every time. With a compressed, searchable memory, a robot can instead recall where it last saw something, the same way you'd remember where you left your glasses instead of checking every room.
Why It Works
Built around one idea.
Recall instead of re-sensing
Query memory directly instead of scanning the room with cameras and sensors again — faster, and it still works even when the object isn't currently in view.
Search by meaning, not metadata
Query compressed perceptual data directly — find the moment that matters without indexing everything up front.
Compress at the edge
Shrink sensor and video streams close to the source, so bandwidth and onboard storage stop being the limiting factor.
Where It's Used
A few concrete use cases.
Task-directed recall
A robot looking for an object queries its memory for where it was last seen, then navigates straight there — instead of re-scanning every room it passes through.
Fleet-scale retrieval
Search across an entire fleet's sensor history for a specific object, scene, or event.
Simulation-to-real datasets
Compress massive training datasets while keeping them fully queryable for downstream model development.
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