Ignacio Gil Moreno
Papers
1
Total Citations
60
H-Index
1
About
Ignacio Gil Moreno is a leading researcher in autonomous systems and robotics, with a primary focus on deep reinforcement learning for vision-based control. His most impactful work addresses the challenging problem of enabling multirotor drones to autonomously land on moving platforms—a critical capability for applications in aerial delivery, maritime operations, and dynamic environments. In his highly cited 2018 paper, "A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform," Gil Moreno pioneered the application of Deep Deterministic Policy Gradients (DDPG) to continuous-action flight control, achieving robust, real-time landing without explicit state estimation. This work, garnering over 60 citations, demonstrates how deep Q-learning principles can be extended from simulated games to real-world physical systems. By integrating raw visual input directly into an end-to-end learning policy, he significantly advanced the field of autonomous aerial robotics. Gil Moreno’s contributions are foundational for researchers exploring safe, adaptive drone navigation in unstructured environments, and his methods continue to influence modern approaches to sensorimotor control and reinforcement learning in robotics.
Research Focus
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Top Papers
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