Projects cover a wide range of problems in optimal control, learning, exploration, manipulation, locomotion, and multi-agent coordination.
Research Experience
Research areas span robotics, optimal control, and learning methods, aiming to develop algorithms that enable robots to reliably learn, explore, and navigate in complex environments.
Background
Research interests include how robots can adapt and learn in real-time when encountering the unknown, particularly through interacting with their environment to explore and learn new scenarios.
Miscellany
The team consists of a diverse group of undergraduate, graduate, and postdoctoral students working on several exciting research fronts.