Mario Otani

Instituto Nacional de Pesquisas da Amazônia

Papers

Total Citations

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About

Mario Otani is a researcher at the forefront of integrating artificial intelligence with robotics and digital twin technologies. His primary research areas encompass reinforcement learning, autonomous navigation, and cyber-physical systems, with a particular focus on applying Deep Q-Learning to robotic control. Otani’s most cited work, "Navigation robot training with Deep Q-Learning monitored by Digital Twin" (2022), introduces a pioneering framework where a vehicular navigation robot learns to transport parts within a constrained environment through deep reinforcement learning, all while being supervised by a digital twin for real-time monitoring and validation. This contribution is significant for bridging simulation and real-world deployment, offering a scalable approach to autonomous logistics. With 3 citations, this paper has already garnered attention in the emerging field of AI-driven robotics. Otani’s work stands out for its practical integration of decision-making algorithms with digital twin architectures, providing a foundation for future research in intelligent manufacturing and autonomous systems. His achievements highlight a commitment to advancing robotics through data-driven, adaptive learning methods.