Alexander Kuleshov

Skolkovo Institute of Science and Technology

Papers

Total Citations

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About

Alexander Kuleshov is a researcher whose work sits at the intersection of machine learning and robotics, with a particular focus on autonomous navigation and spatial awareness systems. His research has made meaningful contributions to the field of appearance-based robot self-localization, exploring how modern computational techniques can enable robots to determine their position and orientation through visual data alone. Kuleshov's most recognized contribution, "Machine Learning in Appearance-Based Robot Self-Localization" (2017, 10 citations), demonstrates his innovative approach to conceptualizing the appearance space — the full range of images a robot's visual system can capture across all possible positions — and leveraging cutting-edge manifold learning and deep learning techniques to navigate this complex representational landscape. This work, complemented by his related study "Mobile Robot Localization via Machine Learning" (2017, 7 citations), establishes a coherent research agenda aimed at making robot localization more robust and data-driven. By framing localization as a machine learning problem rather than a purely geometric one, Kuleshov has helped open new pathways for developing intelligent, adaptive robotic systems. His work will be of particular interest to researchers and students working in computer vision, autonomous robotics, and applied deep learning.