Guelis Montenegro

Pontificia Universidad Católica de Valparaíso, Federico Santa María Technical University

Papers

Total Citations

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About

Guelis Montenegro is a robotics and control systems researcher whose work sits at the intersection of reinforcement learning, autonomous mobile robotics, and simulation-based experimentation. His research focuses primarily on applying deep reinforcement learning (RL) algorithms to solve complex position control problems in mobile robots, addressing the growing inadequacy of traditional control systems in the face of increasingly sophisticated autonomous vehicles and platforms. Montenegro's most influential contribution, "Reinforcement Learning for Position Control Problem of a Mobile Robot" (2020, 29 citations), helped establish RL as a viable framework for autonomous navigation challenges. This work, alongside its 2022 follow-up using the Khepera IV robot within OpenAI Gym and CoppeliaSim (23 citations), demonstrates his commitment to bridging simulated and real-world robotic control. A particularly notable thread in his research is the development of spherical robots — a mechanically challenging platform — both in simulation (20 citations) and as a physically realized prototype with pendulum-based internal mechanics (2023). His work also extends into robotics education, making control concepts accessible to engineering students. Collectively accumulating roughly 90 citations, Montenegro's research meaningfully advances intelligent, simulation-driven approaches to next-generation robotic control systems.