Ivan Nenakhov
Papers
1
Total Citations
3
H-Index
1
About
Ivan Nenakhov is a researcher at the intersection of robotics and computer vision, with a primary focus on enabling robots to continuously learn and adapt to new objects in real-world environments. His key research areas include lifelong learning, object detection, and robotic perception. Nenakhov’s major contribution lies in developing methods that allow robots to incrementally learn to recognize and localize novel objects without forgetting previously acquired knowledge—a critical challenge for autonomous systems operating in human-centric spaces. His most cited work, "Continuous learning with random memory for object detection in robotic applications" (2021), has garnered 3 citations and addresses the practical need for robots to handle unpredictable objects through visual sensing and memory-based learning strategies. This work is notable for its focus on both classification and spatial localization, bridging the gap between continual learning theory and real robotic deployment. Nenakhov’s research is particularly relevant for service robotics, where adaptability to new objects is essential for safe and effective human-robot interaction.
Research Focus
Key Achievements
Top Papers
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