Perception, control, and embodiment aren't separate products here. They're developed against each other from day one, so a robot's body and its intelligence stop working at cross purposes.
6DOF
50Hz
<10ms
24/7
Most robot learning is validated in simulation and quietly falls apart on real hardware. We close that gap by developing against our own actuators and sensors from the start — so sim-to-real isn't something we discover in front of a customer.
The result generalises across tasks using one control architecture, rather than a patchwork of task-specific scripts that each need their own babysitter.
Vision, depth, tactile and proprioceptive streams are fused into a continuously updated estimate of the environment. It holds up when lighting shifts, when objects move, and when the scene stops matching anything in the training set.
High-level intent becomes coordinated, stable motion across every joint. Compliant actuation means unexpected contact is absorbed rather than resisted — safer for people nearby, and less destructive to the hardware.
Actuator selection, sensor placement and structural design are decided alongside the control stack instead of handed to it. Intelligence isn't bolted onto a chassis — the chassis is part of the intelligence.
Locating and grasping a target object under low, uneven lighting — the case that breaks brittle vision stacks.
Bimanual grasp on a deformable object, where grip force matters more than position accuracy.
Teleoperated demonstration capture — how we collect the human motion these policies learn from.
