Oleksii Zhelo
Papers
1
Total Citations
83
H-Index
1
About
Oleksii Zhelo is a researcher at the forefront of deep reinforcement learning (DRL) and autonomous mobile robotics. His work centers on enabling robots to navigate complex, unknown environments without relying on pre-built maps. Zhelo’s most impactful contribution, the 2018 paper “Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning,” has garnered 83 citations and stands as a key reference in the field. In this work, he introduced a novel approach that augments standard external rewards with intrinsic curiosity signals, allowing DRL agents to actively explore their surroundings and learn robust navigation policies. This method effectively addresses the sparse reward problem, enabling robots to discover efficient paths through trial and error. By demonstrating that curiosity can drive learning in mapless settings, Zhelo’s research has opened new avenues for developing more adaptive and resilient autonomous systems. His work is particularly notable for bridging the gap between theoretical DRL advances and practical robotic applications, offering a scalable solution for real-world deployment where maps are unavailable or unreliable.
Research Focus
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Top Papers
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