Svyatoslav Golousov
Papers
3
Total Citations
14
H-Index
2
About
Svyatoslav Golousov is a robotics researcher whose work sits at the intersection of control systems, variable stiffness actuation, and machine learning. His primary research areas include robotic manipulation, walking robot dynamics, and the integration of learning-based methods into traditional control pipelines. Golousov’s major contributions center on the design and control of robotic arms equipped with MACCEPA 2.0 variable stiffness actuators, enabling delicate object manipulation through adaptable compliance. His 2019 paper on this topic, with 8 citations, is foundational for researchers exploring safe human-robot interaction. He further advanced the field by developing a numerical optimisation-based control pipeline for industrial robot arms, designed as a flexible test bench for machine learning experiments—a work that has garnered 5 citations for its practical utility. More recently, Golousov has tackled the challenge of predicting reaction forces in walking robots, analyzing machine learning-based predictor structures to simplify dynamic models. His research demonstrates a clear trajectory from hardware-level control to intelligent, data-driven robotics, making his work relevant for students and researchers interested in bridging classical control theory with modern AI approaches.
Research Focus
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Top Papers
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