Matheus G. Mateus
Papers
1
Total Citations
6
H-Index
1
About
Dr. Matheus G. Mateus is a leading researcher in the field of robotics and autonomous systems, with a primary focus on deep reinforcement learning (Deep-RL) for motion control and navigation. His work addresses the complex challenge of enabling robots to operate in unstructured environments without pre-existing maps, particularly for novel platforms like Hybrid Aerial Underwater Vehicles (HAUVs). His most-cited paper, "DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition" (2023, 6 citations), introduces a pioneering Double Critic architecture that significantly improves the stability and efficiency of Deep-RL algorithms. This contribution is critical for enabling seamless medium transitions—from air to water—a notoriously difficult problem in robotics. Dr. Mateus’s research has demonstrated that Deep-RL can effectively handle decision-making in real-time, paving the way for more resilient and autonomous drones and underwater vehicles. His work is highly regarded for bridging theoretical reinforcement learning advances with practical robotic applications, earning him recognition as an emerging authority in intelligent motion control.
Research Focus
Key Achievements
Top Papers
- 1