Mykola Odnoshyvkin

About

Mykola Odnoshyvkin is a researcher at the forefront of autonomous and mobile robotics, with a specialized focus on robust perception systems for dynamic, high-speed environments. His most cited work, "PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing" (2022, 18 citations), addresses a critical challenge in the field: enabling drones to reliably detect and navigate racing gates without prior real-world training. By developing a deep neural network that achieves zero-shot sim-to-real transfer, Odnoshyvkin’s contribution significantly enhances the practicality of autonomous drone racing, where on-the-fly perception in unknown environments is paramount. This work not only demonstrates his expertise in bridging the simulation-to-reality gap but also underscores his impact on advancing real-world robotic applications. With a growing citation record, Odnoshyvkin’s research is shaping the future of agile, perception-driven autonomous systems, making him a notable figure in robotics and machine learning.

Research Focus

Key Achievements

Top Papers

1. 1
   PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing

   18 citations · 2022

Key Collaborators

Contact & Links