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Driver assistance systems (ADAS) combine information from cameras, lidar, radar, GPS, inertial module, and other sensors to create a comprehensive view of the road. This process, known as sensor fusion, improves the accuracy of perception, object detection, and decision-making in complex environments. However, effective sensor fusion requires precisely synchronized and

The rapid development of industrial automation and the widespread adoption of collaborative robots (cobots) are significantly transforming modern manufacturing processes. This opens up new opportunities to increase productivity, production flexibility, and operational efficiency. In modern robotic solutions, data annotation is becoming increasingly important, especially for systems that use computer vision,

Modern automation of logistical and industrial facilities requires a radical change in artificial intelligence architecture: a transition from egocentric behavioral models to collective interaction systems. Traditional training datasets instill a purely individual logic of actions in a robot, where the machine relies exclusively on its own sensors, independently calculates its

Robotics can manipulate rigid objects, but deformed objects remain a challenge in embodied AI. Objects such as clothing, cables, ropes, bags, and soft materials change shape during interaction. Their behavior depends on gravity, contact forces, friction, and previous states, making perception and manipulation more complex. Robot foundation models require specialized

The rapid advancement of AI and robotic technologies has significantly transformed modern healthcare, particularly in surgery. AI-assisted surgical robotics systems are increasingly being integrated into clinical practice to enhance procedural accuracy, support decision-making, and improve patient outcomes. However, the effectiveness and reliability of these intelligent systems depend heavily on the

Warehouse premises are among the most complex environments for artificial intelligence systems, where standard computer vision algorithms trained on classic open datasets regularly fail. The main challenge here is the organization of the assortment, where thousands of visually identical boxes differing only by a serial number on a tiny barcode

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 gap

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 image

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 actions

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,

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