Robots become part of the new era of automation, studying faster and more effective thanks to the integration of artificial intelligence and industrial metavselnaya. They adapt to various tasks, from the assembly of electronics to the control of complex mechanisms, using analytics in real time and obtained skills. This approach allows robots to accurately predict the date of the next maintenance, optimize energy consumption and improve processes.
Training takes place in “virtual schools” – media modeling real production. Here, robots master complex skills in a matter of hours, which in traditional conditions would take months. This system uses the SIM2Real method, which combines virtual training and its subsequent application in real conditions.
EPF in partnership with siemens I introduced modular components, which allow robots to adapt to changes. This significantly accelerated the development process. At the same time, large studies show that robots trained with large language models and computer vision reach 90% of the success of the tasks even in an unfamiliar environment.
Synthetic data and digital doubles eliminate the deficiency of data for training. For example, the Siemens Simatic Robot Pick AI solution, trained in virtual data, demonstrates an accuracy of more than 98% when performing complex tasks. Similarly, Anybotics creates 3D models of industrial facilities to prepare robots for real conditions with minimal time spent.
These innovations allow companies to accelerate the introduction of robotics and minimize risks. In the future, robots will be able to generate their own tasks based on accumulated knowledge. Such technologies change the approach to automation, allowing companies to adapt to rapid market changes.
Virtual learning and digital double technologies open a new chapter in automation, making robots not just with tools, but independent participants in the production process. The flexibility and adaptability of these decisions promises not only the acceleration of the introduction of innovations, but also the creation of an environment where machines can study, improve and work autonomously, supporting the dynamic needs of the future.