Bogdan Ivanyuk-Skulskiy

National University of Kyiv-Mohyla Academy

Papers

Total Citations

H-Index

About

Bogdan Ivanyuk-Skulskiy is at the forefront of human-robot locomotion, pioneering vision-based systems that allow prosthetic legs and exoskeletons to intelligently navigate complex terrains. His research centers on real-time environmental perception, using egocentric vision and temporal neural networks to predict walking environments before physical interaction occurs. Ivanyuk-Skulskiy’s major contribution is advancing beyond static image classification to incorporate sequential, time-series data—a critical step for enabling smooth, safe transitions between locomotion modes, such as approaching and climbing stairs. His most-cited work, "StairNet: visual recognition of stairs for human–robot locomotion" (2024), has already garnered 23 citations, underscoring its immediate impact in a field where real-world deployment demands split-second accuracy. His subsequent papers on sequential image classification (2023, 2024) further refine these temporal models, collectively establishing a new paradigm for adaptive control in assistive robotics. By bridging computer vision and biomechatronics, Ivanyuk-Skulskiy is not just improving hardware—he is giving robotic limbs the predictive intelligence to see and respond to the world, bringing us closer to seamless human-robot walking.