Technology
Physical AI, from the silicon to the shop floor.
Three layers make a 1to1 robot useful in the real world: a runtime trained on physics, quantum-enhanced sensing that holds its position where classical sensors drift, and a control layer that lets a human step in at any moment.
Core stack
How a unit thinks, senses and obeys
Physical AI runtime
Intelligence that understands mass, friction and consequence.
Our models are trained on physical dynamics — contact, deformation, slip, momentum — rather than on scripted waypoints. A unit that has learned to seat a connector can seat a connector it has never seen, on a surface that moves, without a engineer rewriting a routine. Inference runs on the robot, so a dropped network link degrades bandwidth, not safety.
- On-board inference, no cloud dependency
- Continuous fleet learning across sites
- Hard safety envelope enforced in firmware
0.02s
Decision delta
100%
Local inference
Quantum-enhanced sensing & compute
Tighter state estimates where classical sensors drift.
Quantum inertial sensing sharpens the position and orientation estimate that balance and navigation depend on, which matters most exactly where GPS fails and wheels slip — inside steel structures, on wet docks, on moving decks. Quantum-assisted planning handles the combinatorial routing problems that appear once you have dozens of large machines sharing the same lanes.
- Quantum inertial measurement
- GPS-denied navigation
- Quantum-assisted fleet routing
10×
Drift reduction
GPS-0
Denied-environment ops
Teleoperation and full autonomy
Two control modes, one seat, no retraining.
Every robot we build supports both. An operator can take direct, force-feedback control of any unit from any console, and can hand it back to autonomy mid-task. Autonomy handles the repetitive ninety percent; the operator handles the exceptions. That is what makes a fleet economic before autonomy is perfect.
- Sub-20ms feedback loop
- Instant operator takeover and handback
- One operator supervising many units
- Full session audit trail
14ms
Teleop latency
1:12
Operator to fleet