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Modern robotics relies on simulation environments to accelerate AI development. Rather than relying on physical robots operating in real-world environments, organizations are generating large amounts of synthetic training data in virtual worlds. Simulation platforms allow for experimentation, testing, and large-scale scenario generation. However, a challenge in robotics remains

The rapid development of robotics, AI, and machine learning technologies contributes to the creation of autonomous systems capable of effectively interacting with the environment. One of the key tasks of modern robotics is to ensure reliable capture and manipulation of objects under conditions of uncertainty. To successfully perform such operations,

Unlike simpler automated systems, a humanoid robot's perception is built on a complex fusion of dozens of disparate information streams that must be synchronized in time with millisecond precision. The process of collecting and preparing data for such machines goes far beyond the framework of classic two-dimensional

Instead of manually programming robot behavior, robots are trained using human demonstrations, teleoperation sessions, and multimodal interaction data. This approach allows robots to learn complex manipulation, navigation, and decision-making skills. At the heart of this are simulation learning and scalable annotation datasets. They help machine learning models understand which

The development of AI is increasingly moving beyond the digital realm. While traditional AI systems have primarily worked with text, images, and online data, a new generation of technologies — physical AI — interacts with the real world through robots, autonomous machines, drones, and intelligent industrial systems. Such systems must perceive the

In April 2026, the first comprehensive open dataset of Ukrainian handwritten text - RUKOPYS - was officially released. As its official data annotation partner, Keymakr contributed to the creation of the dataset with hundreds of hours of ground truth labeling.  The dataset was initiated by the Ministry of Economy, Environment, and Agriculture

Even sophisticated machine learning systems can produce misleading results if tested on poorly designed or inconsistent datasets. Therefore, robust benchmark datasets and carefully designed evaluation frameworks are the foundation of modern AI development. Modern AI systems rely on golden dataset methodologies, structured test set generation strategies, and standardized evaluation metrics