KI.FABRIKDeepTech Mechatronics Made in Bavaria
KI.FABRIK©Technical University of Munich. TUM School of Computation, Information and Technology. Chair of Robotics ands Systems Intelligence. Munich Institute of Robotics and Machine Intelligence (MIRMI).
In the collective (Zusammenschluss) of robots, 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.
KI.FABRIK Infrastructure KI.FABRIK Research and Development
KI.FABRIK Infrastructure KI.FABRIK Research and Development
Projects:
KI.FABRIK Research and Development
Affiliations:
Chair of Robotics and Systems Intelligence |
TUM School of Computation, Information and Technology |
Munich Institute of Robotics and Machine Intelligence
Website:
Research Profile
Human workers can work in close proximity to the robot and are still the experts who set up the robot and teach the AI new inspections and the desired position easily without occlusions. As a result, the human worker can focus on richer and more ergonomic tasks.
BMW’s developments at the AI.Society exhibition area of munich_i. ©Messe München
On the front of the vehicle, a human worker teaches the robot to measure a specific gap intuitively with an AR glass. On the front, the inspection system is highly flexible and can be taught or set up in the most intuitive way, just by a gesture.
BMW’s developments at the AI.Society exhibition area of munich_i. ©TUM-MIRMI
On the back of the car, we show collaborative AI-based quality inspection, where the robot can directly communicate with the car. Both systems are fully connected and the robot is built to be set up without any prior robotics knowledge. Quality inspections can be performed highly flexibly on the fly for each car individually.