I believe that prioritizing research into the safety of decision-making systems will enable the swift and responsible real-world deployment of cutting-edge AI advancements. My research is focused on safe and robust imitation and reinforcement learning for robotic systems, with a special emphasis on two key areas: out-of-distribution recovery and generalization through neural ordinary differential equations. This not just about advancing theory; it is about ensuring the practical deployment of safe and reliable policies into a variety of real-world robotic systems.

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