Nikolaos Passalis
Papers
16
Total Citations
147
H-Index
7
About
Nikolaos Passalis is a researcher whose work sits at the intersection of deep learning, computer vision, and autonomous robotics, with a particular focus on making intelligent systems practical, efficient, and safe in real-world environments. His most cited contribution, a novel dataset and evaluation framework for high-resolution water segmentation in Unmanned Surface Vehicles (35 citations), helped establish critical benchmarks for autonomous maritime navigation. This maritime thread continues through his involvement in the AutoSOS project (23 citations), which explored multi-UAV systems for search and rescue operations using lightweight AI and edge computing. A central theme in Passalis's research is enabling robots to perceive their environments actively rather than passively — demonstrated through his work on deep reinforcement learning for face recognition and active vision control policies (9–10 citations). His development of OpenDR (24 citations), an open toolkit bridging deep learning and robotics, reflects his commitment to accessible, high-performance AI with a low computational footprint. More recently, his involvement in the RoboSAPIENS project (10 citations) highlights a growing interest in trustworthy, adaptive robotic systems. Collectively, his work advances the frontier of embodied AI, offering tools and frameworks that help robots operate intelligently and safely in complex, unpredictable settings.
Research Focus
Key Achievements
Top Papers
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- 4Neural networks and backpropagation18 citations · 2022
- 5Robotic safe adaptation in unprecedented situations: the RoboSAPIENS project10 citations · 2024
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- 8Lightweight deep learning3 citations · 2022
- 9Deep Learning for Active Robotic Perception2 citations · 2023
- 10