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About

Elia Kaufmann is a pioneering robotics researcher whose work sits at the intersection of autonomous flight, deep reinforcement learning, and agile control systems. Best known for his landmark 2023 paper "Champion-level drone racing using deep reinforcement learning" (562 citations), Kaufmann made international headlines by developing an AI system capable of defeating professional human pilots in first-person view drone racing — a remarkable milestone in autonomous systems research. His broader contributions span trajectory optimization, neural model predictive control, and vision-based flight, with his "Real-Time Neural MPC" work (191 citations) demonstrating how deep learning can dramatically improve embedded control performance on quadrotors. Kaufmann has also made significant contributions to the research community through open tools and platforms, including the Flightmare quadrotor simulator and the Agilicious open-source hardware platform, lowering barriers for agile flight research worldwide. His early exploration work on multi-rotor frontier selection (238 citations) further underscores his range across both fundamental autonomy problems and high-performance applications. Notably, his methods have extended beyond drones into autonomous car racing, achieving superhuman performance in Gran Turismo Sport. Collectively, his work has accumulated over 1,400 citations, cementing his reputation as a leading voice in agile autonomous systems.