Currently, everything revolves around chatbots. With ChatGPT, the topic of artificial intelligence (AI) has become socially relevant and much discussed in a very short period of time. Industry has been researching this topic for several years. One focus: robot programming. "To a large extent, programming a robot for a task is still reserved for experts," says Roland Ritter, Platform Program Manager Simulation at KUKA. "This is precisely why we are working on an AI chatbot that translates a simple voice command into programming code." The AI model then generates the code that causes the robot to do exactly that from 'Grab the components one by one and place them in a U-shape on the table'.
At the moment, this is all still happening in a simulated environment. "We could transfer the AI-generated code to the robot controller, but that is currently still too unsafe. The entire industry agrees on this," explains Ritter. The robot's digital twin steps in to check whether the AI-generated robot program is error-free.
Swisslog's AI model recognizes waste and distinguishes shampoo from shower gel
Food companies such as REWE, drugstore giants such as DM and companies from the pharmaceutical industry are among the customers of Swisslog, KUKA's Swiss intralogistics company.
On average, these customers have several 10,000 different products in their assortments, packed in bags, boxes or even without outer packaging. "Every day, these different items have to be picked, i.e. put together for a customer or delivery order - and as error-free as possible," says Niklas Goddemeier. He is Head of Research & Development at the Robogistic Product Center at Swisslog. "To ensure that the individual products are put together correctly and that no leftover packaging finds its way to the customer, we have trained an AI model so that it can not only recognize waste, but also distinguish shampoo bottles from shower gel bottles."
AI support is a natural choice for image-based robot systems. The only question that remains at the moment is: how do I allow such systems to continue learning? In summer, the customer probably has a lot of products in bags, in winter in boxes. So how do you ensure that the AI doesn't forget how the gripper on the robot arm has to grip boxes in winter? This topic of "model updates" is currently still on many people's minds.
