The Furtherance of Autonomous Engineering via Reinforcement Learning

Engineering efforts are one of the major cost factors in today’s industrial automation systems. We present a configuration system, which grants a reduced obligation of engineering effort. Through self-learning the configuration system can adapt to various tasks by actively learning about its environment. We validate our configuration system using a robotic perception system, specifically a picking application. Perception systems for robotic applications become increasingly essential in industrial environments. Today, such systems often require tedious configuration and design from a well trained technician. These processes have to be carried out for each application and each change in the environment. Our robotic perception system is evaluated on the BOP benchmark and consists of two elements. First, we design building blocks, which are algorithms and datasets available for our configuration algorithm. Second, we implement agents (configuration algorithms) which are designed to intelligently interact with our building blocks. On an examplary industrial robotic picking problem we show, that our autonomous engineering system can reduce engineering efforts.