ZaiNar Complementing Onboard Perception

The key point is that ZaiNar is trying to become shared infrastructure for robot fleets, not the brain inside each robot. Cameras and onboard perception tell one robot what is in front of it right now. ZaiNar adds a site wide location layer that keeps tracking robots, vehicles, and assets across walls, underground areas, and other places where cameras have blind spots, so every machine can start from the same map of where things are.1 2 3 4

• Arm is positioning Physical AI as a full stack problem across compute, software, sensors, cloud, and digital twins. That makes ZaiNar less like a camera vendor and more like a middleware layer that plugs into many robot types and feeds location data into higher level planning and simulation systems.2 3
• The practical division of labor is clear in robotics. Vision companies like Orbbec help a robot see depth and objects near the machine, while broader robot stacks like Figure or Bright Machines combine cameras with control software for local task execution. ZaiNar sits above that, supplying shared coordinates instead of doing object recognition or manipulation itself.4 5 6
• That shared layer matters most in multi robot and industrial settings, where every machine otherwise has to solve positioning alone. ZaiNar has framed this as moving location from each robot into the network, which can reduce hardware added to each machine and make fleet level analytics, digital twins, and training data easier to build.1 7

This points toward robotics stacks where onboard perception handles instant local decisions, while shared infrastructure handles site memory and coordination. If that model spreads, ZaiNar can become a standard location layer for warehouses, mines, factories, and autonomous fleets, embedded underneath many robot applications rather than tied to any single robot maker.1 2 3 7