Anastasios M. Lekkas
Papers
11
Total Citations
109
H-Index
6
About
Anastasios M. Lekkas is a leading researcher at the intersection of explainable artificial intelligence (XAI) and robotics, with a particular focus on marine and autonomous systems. His work addresses a critical challenge in modern robotics: the black-box nature of deep neural networks used in deep reinforcement learning (DRL). Lekkas has pioneered the application of model tree methods and Shapley values—both causal and marginal—to generate real-time, interpretable explanations for robotic control policies. His research spans robotic lever manipulation, automatic docking for marine vessels, and multi-robot inspection and maintenance operations for offshore oil and gas platforms. With over 100 citations across his most-cited works, including papers from 2021 to 2024, his contributions have been recognized for advancing trust and safety in autonomous systems. Notably, his 2021 study on explainable DRL for automatic docking and his 2022 work on model tree methods for real-time robotic applications have garnered significant attention. Lekkas’s ongoing projects on multi-robot autonomy and temporal planning for underwater intervention drones further underscore his commitment to deploying reliable, interpretable AI in high-stakes environments.
Research Focus
Key Achievements
Top Papers
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- 7Comparison of AI Planning frameworks for underwater intervention drones6 citations · 2020
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