Sim-to-Real transfer is the process of training artificial intelligence to control a robot inside a massively parallel, physics-accurate video game simulation, and then downloading that fully trained "brain" into a physical robot so it can walk, grab, and navigate the real world without having to learn from scratch.
Neuromorphic vision sensors, or event-based cameras, are bio-inspired digital eyes that ignore static backgrounds and exclusively process asynchronous pixel-level changes in light, allowing artificial intelligence to react in microseconds.
A robotics foundation model is a massive artificial intelligence system that translates visual data and human commands directly into physical movement, allowing machines to navigate the real world without hardcoded programming.
A force-torque sensor matrix relies on microscopic foil grids glued to a metal chassis to physically measure how the metal bends under pressure, mathematically calculating the exact force and rotational torque a robotic arm is applying to an object in real time.
A vision-language-action policy network directly translates camera pixels and textual commands into continuous mathematical signals that physically drive robotic motors, eliminating millions of lines of traditional hard-coded control software.