Gabriel Moraes Barros

Universidade Estadual de Campinas (UNICAMP)

Papers

Total Citations

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About

Gabriel Moraes Barros is an emerging researcher specializing in autonomous robotics, unmanned aerial vehicles (UAVs), and machine learning-based control systems. His work sits at the intersection of reinforcement learning and robotics, with a particular focus on enabling intelligent, adaptive behavior in aerial platforms. Barros has made notable contributions to the application of advanced reinforcement learning algorithms for UAV control. His 2020 paper on reinforcement and imitation learning for autonomous aerial robot control explores how robots can learn, adapt, and reproduce complex tasks through autonomous exploration — a critical challenge in modern robotics. Complementing this, his work on applying the Soft Actor-Critic (SAC) algorithm to low-level UAV control addresses the fundamental instability challenges inherent in drone systems, proposing learning-based alternatives to traditional control approaches. Both papers, each accumulating 4 citations, reflect a growing scholarly interest in replacing conventional UAV control methods with more flexible, data-driven solutions. For students and researchers working in autonomous systems, drone technology, or applied machine learning, Barros's contributions offer practical insights into how state-of-the-art reinforcement learning techniques can be deployed in real-world aerial robotics applications.