H.B. Keller Colloquium
Jonathan P. How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He received a B.A.Sc. (aerospace) from the University of Toronto in 1987, and his S.M. and Ph.D. in Aeronautics and Astronautics from MIT in 1990 and 1993, respectively, and then studied for 1.5 years at MIT as a postdoctoral associate. Prior to joining MIT in 2000, he was an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the American Institute of Aeronautics and Astronautics (AIAA). He was elected to the National Academy of Engineering (NAE) in 2021. Dr. How was the editor-in-chief of the IEEE Control Systems Magazine (2015-19) and is an associate editor for the AIAA Journal of Aerospace Information Systems and the IEEE Transactions on Neural Networks and Learning Systems. He was elected to the Board of Governors of the IEEE Control Systems Society (CSS) in 2019 and is a member of the IEEE CSS Technical Committee on Aerospace Control and the Technical Committee on Intelligent Control. He is the Director of the Ford-MIT Alliance and was a member of the USAF Scientific Advisory Board (SAB) from 2014-17. Dr. How’s research focuses on robust planning and learning under uncertainty with an emphasis on multiagent systems, and he was the planning and control lead for the MIT DARPA Urban Challenge team in 2007. His work has been recognized with multiple awards, including the 2020 IEEE CSS Distinguished Member Award, the 2020 AIAA Intelligent Systems Award, the 2002 Institute of Navigation Burka Award, the 2011 IFAC Automatica award for best applications paper, the 2015 AeroLion Technologies Outstanding Paper Award for Unmanned Systems, the 2015 winner of the IEEE Control Systems Society Video Clip Contest, the IROS Best Paper Award on Cognitive Robotics (2017 and 2019), the 2020 ICRA Best Paper Award in Service Robotics, and three AIAA Best Paper in Conference Awards (2011-2013). He received the Amazon Machine Learning Research Award in 2018 and 2020, and he was awarded the Air Force Commander’s Public Service Award in 2017 for his contributions to the SAB. Dr. How serves on the AIAA Fellows Committee for Information Systems and Systems Integration and is the 2026-2028 president elect for the IEEE Control System Society.
Unmanned aerial systems hold promise for critical applications including search and rescue, environmental monitoring, and autonomous delivery. Real world deployment in safety critical settings, however, remains challenging due to GPS denied operation, uncertainty in perception, and the need for safe trajectory planning in dynamic, partially known environments. This talk presents recent advances in planning, control, and perception that together enable robust, scalable, and efficient aerial autonomy. On the planning and control side, I first present IL-RTMPC, a demonstration and training efficient approach for learning robust control policies from model predictive control. By combining single trajectory demonstrations with disturbance aware data aggregation, IL-RTMPC produces policies that generalize to unseen conditions, with validation on quadrotors and the MIT SoftFly platform. I then introduce DYNUS, which enables uncertainty aware trajectory planning for safe, real time flight in dynamic and unknown environments. Building on this foundation, MIGHTY performs fully coupled spatiotemporal optimization to generate agile and precise motion by jointly reasoning about path and timing. Together with prior work on Robust MADER, these methods enable fast, safe, multi robot navigation under uncertainty. On the perception side, I introduce complementary mapping frameworks that support long term autonomy and planning. GRAND SLAM combines 3D Gaussian splatting with semantic and geometric priors to produce unified scene representations suitable for photorealistic planning. ROMAN compresses environments into sparse, object centric maps that are orders of magnitude smaller than traditional representations while still enabling accurate relocalization and loop closure under extreme viewpoint changes. I also discuss the interaction between perception and control, focusing on safety filtering for systems that rely on learned perception models. I will present results across simulation and hardware experiments and conclude with open challenges in building resilient autonomous aerial systems. These advances move us closer to reliable UAS autonomy with meaningful real world impact.
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