Collective Learning

In the robot collective, all robots can exchange their knowledge about their respective task. This exchange enables the robots to learn much faster than unconnected robots. An example is the insertion skill. With this skill, robots can insert keys into keyholes, plugs into sockets or bolts into boreholes. As this is a highly tactile problem, it is just solvable for a sensitive robot. Connecting robots in such a collective not just speed up the total learning success, but also enables the robots to converge to more general solutions. As the robots can draw on a diverse set of solutions within the collective, more general solutions are promoted but also optimized, and individual solutions are possible. A single robot would take ~1.5 hours to learn on its own and that would just result in being able to insert this special key into the learned hole. When the whole collective learns similar tasks, the solution is more general and the learning is much faster. You can see the fully connected AI-powered production system of the future. Robots are working closely together with humans to make production more ergonomic and efficient.