Training robot manipulation policies that generalize across diverse component geometries without reprogramming.
Policies trained in physics simulators under randomized friction, mass, and lighting conditions transfer directly to physical cobots, handling wire harnesses and flexible gaskets robustly.
Operators guide the cobot arm through an assembly sequence once; the neural network extracts the underlying goal trajectory, automatically adapting to varying part positions.
In an automotive interior assembly line with 45 distinct trim variants, adaptive policy networks eliminated custom tooling changeovers, saving $2.4M annually.