Carlos Sampedro
Centre for Automation and Robotics, Universidad Politécnica de Madrid
Papers
15
Total Citations
1,330
H-Index
13
About
Carlos Sampedro is a leading researcher in aerial robotics, whose work has fundamentally advanced the integration of deep learning and autonomous flight. His primary research areas span deep reinforcement learning, computer vision, and human-drone interaction, with a specific focus on enabling fully autonomous operations for unmanned aerial vehicles (UAVs). His seminal 2017 review on deep learning methods for UAVs, cited over 360 times, has become a foundational resource for the field. Sampedro has made groundbreaking contributions to autonomous landing, developing deep reinforcement learning strategies that allow multirotor drones to land safely on moving platforms—a critical capability for maritime and search-and-rescue operations. His work on fully-autonomous aerial robots for indoor search and rescue (182 citations) demonstrates the real-world impact of his learning-based techniques. Additionally, he pioneered natural user interfaces for human-drone interaction (176 citations), making drones more accessible through intuitive multimodal control. As the lead architect of the AEROSTACK open-source software framework, Sampedro has provided the robotics community with a powerful, multi-layered architecture for developing and deploying complex aerial robotic systems. His research, totaling over 1,200 citations, continues to shape the future of autonomous drones.
Research Focus
Key Achievements
Top Papers
- 1A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles363 citations · 2017
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- 4Natural user interfaces for human-drone multi-modal interaction176 citations · 2016
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