Sergio A. Serrano

National Institute of Astrophysics, Optics and Electronics

Papers

Total Citations

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About

Sergio A. Serrano is a leading researcher in artificial intelligence and robotics, specializing in reinforcement learning, knowledge transfer, and autonomous navigation. His work addresses critical challenges in making AI systems more efficient and adaptable across domains. Serrano’s most influential contribution is his systematic review on knowledge transfer for cross-domain reinforcement learning (2024, 19 citations), which provides a comprehensive framework for reducing the data demands of RL agents—a key bottleneck in real-world deployment. He also advanced task planning under uncertainty with his work on knowledge-based hierarchical POMDPs (2021, 18 citations), integrating structured knowledge into probabilistic models for more robust decision-making. Earlier in his career, Serrano tackled sensor fusion for robot navigation in dynamic environments (2019), combining RGB-D cameras and 2D laser data to improve autonomous mobility. His research bridges theoretical foundations and practical applications, offering scalable solutions for robotics and AI. With a growing citation impact, Serrano’s work is essential reading for students and researchers seeking to understand how knowledge transfer and hierarchical planning can unlock more efficient, cross-domain AI systems.