My main research interest is deep reinforcement learning and imitation learning, as applied to robotics. In general, I like thinking about what’s necessary to make robot learning work for real-world problems. Much of my work has been based on how to most efficiently use real robot time, how to leverage external data sources that aren’t bottlenecked by real robot hardware, and the challenges of longterm agent-centric data collection.