Nataliya Strokina

Papers

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About

Nataliya Strokina is a leading researcher in field robotics, with a primary focus on autonomous systems for challenging environments. Her work bridges perception, learning, and control, with key contributions in underwater robot localization and heavy machinery automation. In her foundational work on underwater robotics, Strokina pioneered the use of bio-inspired flow sensors for localization, demonstrating that flow feature extraction can provide critical perceptual information where conventional vision and sonar systems fall short—a paper that has garnered 14 citations and opened new avenues for non-visual underwater navigation. Strokina has made significant strides in autonomous construction and mining equipment. Her 2020 study on learning-based pile loading controllers for robotic wheel loaders introduced a method to learn controller parameters from a small number of human demonstrations, using low-level sensor data and egocentric video. This work, with 7 citations, directly addresses the challenge of deploying learning systems in field robotics where data is scarce. More recently, her 2022 paper tackled the critical problem of reward function design in reinforcement learning for sequential tasks, proposing a visual reward framework that enables autonomous pile loading without abundant datasets. With 5 citations, this work represents an important step toward practical, real-world deployment of RL in unstructured environments. Strokina’s research continues to push the boundaries of what autonomous robots can achieve in complex, real-world settings.