Papers
18
Total Citations
480
H-Index
8
About
Ignacio Carlucho is a robotics and control systems researcher whose work sits at the intersection of deep reinforcement learning, adaptive control, and autonomous robotics. He is best known for pioneering the integration of reinforcement learning with classical PID control architectures, particularly for mobile robots — a body of work that has accumulated over 300 citations across three highly influential papers. His 2020 paper on adaptive deep reinforcement learning for MIMO PID control of mobile robots stands as his most impactful contribution, earning 147 citations and demonstrating how neural learning can dynamically tune control parameters in complex, multi-input systems. Carlucho has also made significant strides in underwater robotics, addressing the notoriously difficult challenges of autonomous underwater vehicle (AUV) navigation, where unknown dynamics and tight coupling between degrees of freedom make traditional control unreliable. His work on end-to-end deep reinforcement learning for AUV position tracking and adaptive controllers for underwater manipulators reflects a sustained commitment to marine autonomy. More recently, he has expanded into digital twin frameworks for ROV teleoperation and benchmark tools like URoBench, helping standardize how the field evaluates underwater robotics simulators — essential infrastructure for future research progress.
Research Focus
Key Achievements
Top Papers
- 1
- 2Incremental Q -learning strategy for adaptive PID control of mobile robots99 citations · 2017
- 3Double Q-PID algorithm for mobile robot control78 citations · 2019
- 4AUV Position Tracking Control Using End-to-End Deep Reinforcement Learning50 citations · 2018
- 5
- 6Digital Twins Below the Surface: Enhancing Underwater Teleoperation12 citations · 2024
- 7
- 8MACÁBOT: Prototipo de Vehículo Autónomo de Superficie (ASV)8 citations · 2019
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- 10