Skill-based programming has proven to be advantageous for assembly tasks, but still requires expert knowledge, especially for force-controlled applications. However, it is error-prone due to the multitude of parameters, e.g. different coordinate frames and either position-, velocity- or force-controlled motions on the axes of a frame. We propose a mixed reality based solution, which systematically visualizes the geometric constraints of advanced high-level skills directly in the real-world robotic environment and provides a user interface to create applications efficiently and safely in mixed reality. Therefore, state-machine information is also visualized, and a holographic digital twin allows the user to ad-hoc validate the program via force-enabled simulation. The approach is evaluated on a top hat rail mounting task, proving the capability of the system to handle advanced assembly programming tasks efficiently and tangibly.

Increasingly volatile markets challenge companies and demand flexible production systems that can be quickly adapted to new conditions. Machine Learning has proven to show significant potential in supporting the human operator during the time-consuming and complex task of robot pro-gramming by identifying relevant parameters of the underlying robot control program. We present a solution to learn these parameters for contact-rich, force-controlled assembly tasks from a simulation using hardware-independent robot skills. We show that successful learning and real-world execution are possible even under process deviation and tolerances utilizing the designed learning system. We present learning skill param-eters as high-level robot control, evaluation and comparison of extensive simulations, and preliminary experiments on a physical robot test-bed. The developed solution approach is evaluated and discussed using the Peg-in-Hole process, a typical benchmark process in force-controlled assembly.

The ambitious requirements for the economical production of personalized products are one of the key drivers in the development of flexible automation solutions. Novel solution approaches for programming robots using advanced Machine Learning methods show promising results to increase flexibility. These so called intelligent automation solutions can adapt to changes in the product or manufacturing process with only minor human investment. However, the significant amounts of data needed to train the robots still limits the approach. One possible way is to generate this data is utilizing a physics simulation environment. Specialized joining models can be used to further increase the accuracy of the simulation and enrich the generated data as well as reduce the gap between the simulation and the real world. The presented work focussed on the extension of state of the art simulation environments with dedicated joining models for the assembly of electrical components in the electrical cabinet assembly. This extension is intended to make a significant contribution to Simulation-based training for intelligent robot-based automation solutions.