Sebastian Scherer

Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments.

Education:

• Ph.D. in Robotics, Carnegie Mellon University (2010)
• MS in Robotics, Carnegie Mellon University (2007)
• BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004)

His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy. He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010).

His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround, BETTY, and SubT-MRS, and simulation tools like Pegasus Simulator, indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments.

Scientific Awards:

• Popular Science Best of What's New 2010 Award
• AIAA@Infotech Best Paper Runner-up Award (2010)
• Siebel Scholar

Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles.

He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.