Adaptive Autonomy

Autonomous systems may need to operate under different conditions than they have been designed for (e.g., unforeseen faults, different environmental settings, etc.). To deal with this problem, the systems need to be able to adapt their models to new situations without the need for large amounts of data. Our work has focused on leveraging meta-learning and Bayesian techniques to provide adaptation to unforeseen conditions.

Relevant Publications:

• A. Yildiz, E. Yel, A. Corso, K. Wray, S. Witwicki and M. Kochenderfer, “Experience filter: Transferring past experiences to unseen tasks or environments”, IEEE Intelligent Vehicles Symposium (IV) 2023 PDF
• E. Yel, Shijie Gao, N. Bezzo, ”Meta-Learning-based Proactive Online Planning for UAVs under Degraded Conditions”, (*equal contribution), Robotics and Automation Letters (RA-L), 2022 PDF
• E. Yel, N. Bezzo, ”A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 PDF
• E. Yel, N. Bezzo, ”GP-based Runtime Planning, Learning, and Recovery for Safe UAV Operations under Unforeseen Disturbances” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020 PDF