In the future, robots will be doing everything for us, right?<\/p>\n\n\n\n
Well, maybe not quite everything. But they\u2019ll most likely be doing a lot. In this blog, we’ll explore how combining simulation with real-world testing will revolutionize robot training and development.<\/p>\n\n\n\n
Many factories already have robots performing a wide range of automated tasks that boost productivity. At CES 2026, Siemens and NVIDIA announced that the Siemens Xcelerator platform will be used to design and simulate factory digital twins.<\/a> Some manufacturers have four legged robots that walk the factory floor checking systems and ensuring no malfunctions are imminent. For example, a brewery in Leuven uses a robot to check for mechanical issues and air leaks<\/a>. It carries out 1,800 inspections per week and detected 150 anomalies in the first six months. This works because the robot follows a pre-programmed route on a flat surface with no obstacles.<\/p>\n\n\n\n What about autonomous robots that can freely move around and carry out more complex activities?<\/p>\n\n\n\n These could be used in all sorts of scenarios such as defense or rescue missions where they can scan areas that would be dangerous for humans to enter. They could ensure that any potential problems are identified and dealt with to guarantee safety.<\/p>\n\n\n\n But sending a robot out \u201cinto the wild\u201d presents a new challenge: How does it navigate unknown terrain? This is where advanced robot training methods become critical.<\/p>\n\n\n\n In an enclosed site with a defined route you know exactly where it will be going, what the surface is made of, where there are steps or ramps. To be useful in a wider unmapped area it needs to be able to cope with unexpected obstacles and surface changes, stay on its feet, and reach its target.<\/p>\n\n\n\n The answer to this problem is effective robot training. Modern, intelligent robots are designed to learn from their experiences and adjust their behavior accordingly.<\/p>\n\n\n\n So, all they need is data to learn from. The trouble is, they need a lot of data.<\/p>\n\n\n\n Capturing this data purely through real-world testing would take an incredibly long time. And it would also be dangerous and very costly. Just like humans, robots learn from making mistakes. But in this scenario, a mistake will often lead to the robot falling over and damaging itself, incurring expensive repairs and adding further delays to an already lengthy timeline.<\/p>\n\n\n\nThe role of data in robot training<\/h2>\n\n\n\n