Papers

7

Total Citations

103

H-Index

5

About

Katerina Maria Oikonomou is a pioneering researcher at the intersection of robotics, reinforcement learning, and neuromorphic computing. Her work centers on developing energy-efficient robotic systems by integrating spiking neural networks (SNNs) with reinforcement learning algorithms—a biologically inspired approach that dramatically reduces power consumption compared to traditional deep neural networks. Her most influential contributions include a hybrid reinforcement learning framework with a spiking actor network for robotic arm target reaching (31 citations) and a comprehensive survey on technical challenges in assistive robotics for elderly care, forming the foundation of the ASPiDA concept (31 citations). She has also advanced object manipulation through hybrid SNN reinforcement learning agents (18 citations) and developed novel approaches for traversability estimation through autonomous robot experimentation (12 citations). Her research addresses critical challenges in assisted living, aerial robotics, and domestic environments, where energy efficiency and real-time adaptation are paramount. With a growing citation record spanning from 2019 to 2025, Oikonomou has established herself as a leading voice in making autonomous robots more practical, sustainable, and capable of operating in dynamic, real-world settings.

Research Focus

Key Achievements

5
H-Index
7
Papers
103
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
A Hybrid Reinforcement Learning Approach With a Spiking Actor Network for Efficient Robotic Arm Target Reaching
31 citations · 2023
📈 Most Prolific Year: 2023 (4 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: Democritus University of Thrace, National Centre of Scientific Research "Demokritos"

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago