Robots that understand reality, ground themselves within it, and change it.
Robots that understand reality,ground themselves within it,and change it.
in realityPHYSICALREALITY,Inc.
Our mission
in reality
General robotics is fundamentally two problems, both unsolved.
We are solving both:
Modeling reality precisely enough to think and know how to change it.
Understanding oneself well enough to change it effectively.
What we do
We build the software that lets machines know, think, and act, in the real world.
Universal reality decoderDeeply understand reality: turn robot, human, or sim data into complete, richly decomposed training data. What happened (captions), why (reasoning traces), what could have happened (counterfactuals), what actions the scene affords (semantic and physical affordances), where everything moved (2D/3D tracking), how it translates to your embodiment (retargeting), and more.03.2Synthetic training environmentsDeeply understand yourself: generate orders of magnitude more on-policy training experience per dollar than a real robot fleet. Search & synthesize rare, adversarial, or out-of-distribution scenarios endlessly. Shape exactly the behaviors a task requires. Train around the clock toward peak performance afforded by an embodiment.03.3Evaluation infrastructureKnow where you stand: ground research and product direction in trustworthy, repeatable measurement of real-world performance. Know what your robot can do, could do, and never stop moving the goalposts forward.
Understand it
Understanding is not a spectator’s summary of the scene. It is working knowledge: the world modeled precisely enough to act on, from exactly where the machine stands.
Knowledge indexed to the robot’s own situation: this frame, this joint, this contact, now.
What is here?
Where am I?
counterfactual
What should I do?
What should I have done?
Change it
Inference reduces to data; gross physical action is indexical, self-inclusive, and recursive. Put plainly: you can't learn to act purely from the sidelines (off-policy) or after the fact (offline). It has to be you, in the world, and every move changes what comes next.
This makes action the second problem, not a footnote to the first.
The strange loop of understanding & action
The field currently tries to do both at once, but lacks the tools to do either well. We tackle them in sequence.
Understanding: model reality precisely enough to act on it. Pushed far enough, performance converges and becomes only a question of scale and granularity.
Action: is of a different kind. No system can model itself fully and actions are irreversible. The future is not precisely calculable by those taking part in changing it. Larger models don't fix that, but learning the loop itself does. Observe, understand, act, repeat.
Understand reality.
Change it.
We are building the software layer between what a machine perceives and what it chooses to do next.
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