My life goal is to fulfill the science fiction vision of machines that achieve human levels of competence in perceiving, thinking, and acting. A more narrow technical goal is to understand how to get machines to generate and perceive human behavior. I use two complementary approaches, exploring humanoid robotics and human aware environments. Building humanoid robots tests our understanding of how to generate human-like behavior, and exposes the gaps and failures in current approaches. Building human aware environments (environments that perceive human activity and estimate human internal state) pushes the development of machine perception of humans. In addition to being socially useful, building human aware environments helps us develop humanoid robots that are capable of understanding and interacting with humans.

Machine learning underlies much of my work in both humanoid robotics and human aware environments. I am an experimentalist in the field of robot learning, specializing in the learning of challenging dynamic tasks such as juggling. I combine designing learning algorithms with exploring their behavior in implementations on actual robots and in intelligent environments. My research interests include nonparametric learning, memory-based learning, reinforcement learning, learning from demonstration, and modeling human behavior.