Mariano De Paula

About

Mariano De Paula is a prominent researcher specializing in autonomous robotics, adaptive control systems, and reinforcement learning-based navigation, with a particular focus on mobile and underwater vehicles. His work sits at the intersection of machine learning and classical control theory, pioneering the integration of deep reinforcement learning with PID control architectures to address the challenges of dynamic, uncertain environments. De Paula's most influential contributions include developing adaptive Q-learning and deep reinforcement learning strategies for mobile robot control, culminating in his highly cited 2020 paper on MIMO PID control (147 citations) and his foundational 2017 incremental Q-learning approach (99 citations). These works have significantly shaped how researchers design intelligent, self-tuning controllers for autonomous systems. His research extends to autonomous underwater vehicles (AUVs), where he tackled the notoriously difficult problems of coupled dynamics and unknown model parameters, earning substantial recognition with over 90 combined citations across his AUV-focused studies. Beyond theoretical contributions, De Paula has demonstrated a strong commitment to practical implementation, developing physical platforms such as the MACÁBOT surface vehicle and the Ictiobot-40 low-cost AUV. His more recent work on ROS-based digital twins reflects an evolving interest in smart manufacturing robotics. With over 460 total citations, his research continues to meaningfully advance autonomous systems engineering.

Research Focus

Key Achievements

Top Papers

1. 1
   An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots

   147 citations · 2020
2. 2
   Incremental Q -learning strategy for adaptive PID control of mobile robots

   99 citations · 2017
3. 3
   Double Q-PID algorithm for mobile robot control

   78 citations · 2019
4. 4
   AUV Position Tracking Control Using End-to-End Deep Reinforcement Learning

   50 citations · 2018
5. 5
   Trajectory tracking algorithm for autonomous vehicles using adaptive reinforcement learning

   41 citations · 2015
6. 6
   ROS-based architecture for fast digital twin development of smart manufacturing robotized systems

   27 citations · 2022
7. 7
   An obstacle avoidance system for mobile robotics based on the virtual force field method

   10 citations · 2018
8. 8
   MACÁBOT: Prototipo de Vehículo Autónomo de Superficie (ASV)

   8 citations · 2019
9. 9
   Ictiobot-40 a low cost AUV platform for acoustic imaging surveying

   2 citations · 2019

Key Collaborators

Contact & Links