MMAE Seminar Series: ‘Optimality and Guarantees in Robotic Exploration’

Headshot of Ian Abraham, assistant professor of mechanical engineering at Yale University.The Department of Mechanical, Materials, and Aerospace Engineering presents their spring 2024 seminar series featuring guest speaker Ian Abraham, Assistant Professor in Mechanical Engineering with courtesy appointment in the Computer Science Department at Yale University, who will present “Optimality and Guarantees in Robotic Exploration.” This seminar is open to the public and will take place on Wednesday, April 17, 2024, from 3:30–4:30 p.m. in room 104 of the Rettaliata Engineering Center.


Guaranteeing effective exploration is a vital component in the success of robotic applications in ocean and space exploration, environmental monitoring, and search and rescue tasks. This talk presents a novel formulation of exploration that permits optimality criteria and performance guarantees for robotic exploration tasks. We define the problem of exploration as a coverage problem on continuous (infinite-dimensional) spaces based on ergodic theory and derive control methods that satisfy optimality and guarantees such as asymptotic coverage, set-invariance, time-optimality, and reachability in exploration tasks. Last, we demonstrate successful execution of the approach on a range robotic systems.


Ian Abraham is an Assistant Professor in Mechanical Engineering with courtesy appointment in the Computer Science Department at Yale University. His research group is focused on developing real-time optimal control methods for data-efficient robotic learning and exploration. Before joining Yale, he was a postdoctoral researcher at the Robotics Institute at Carnegie Mellon University in the Biorobotics Lab. He received his PhD. and M.S. degrees in Mechanical Engineering from Northwestern University and the B.S. degree in Mechanical and Aerospace Engineering from Rutgers University. During his Ph.D. he also worked at the NVIDIA Seattle Robotics Lab where he worked on robust model-based control for large parameter uncertainty.

His research interest lies at the intersection of robotics, optimal control, and machine learning with a focus on developing real-time embedded software for exploration and learning. He is the recipient of the 2023 Best Paper Award at the Robotics: Science and Systems conference, the 2019 King-Sun Fu IEEE Transactions on Robotics Best Paper award, the Northwestern Belytschko Outstanding Research award for his dissertation, and the 2023 NSF CAREER award.