Even though digitalization is a topic we’re now quite familiar with, there are still some issues to be resolved. One challenge is the subject of communication: each robot has its own operating system. Our grippers should theoretically be able to do anything. Therefore, one goal for the future is to enable simpler communication between grippers and the robot control system. Furthermore, lot of our attention is presently focused on the issue of how the collected data can be analyzed. The data only becomes valuable if the right information can be extracted from it – in real time where possible. Machine learning (ML) and artificial intelligence (AI) are playing an increasingly important role in this regard. We have already entered into the field of ML – with monitored learning processes for error classification in the vacuum system and regression models for wear forecasting for predictive maintenance. But that’s just the start. It gets really interesting when the grippers themselves can learn and therefore autonomously adapt to changing or unfamiliar workpieces. To do so, however, they need to gather more information, for instance, by connecting to a camera system. That is also important when it comes to separating objects that are supplied in a chaotic way, which requires hand-eye coordination. There is much potential for development here, too. As you can see, digitization has opened the door to a whole new world for us, which we are gradually discovering and exploring.