From Narrow Robots to General Robots
Despite the accelerating progress in robotics, robots today remain relatively narrow in their capabilities. To have robots that can work seamlessly with humans, I will advocate building "generalist robots" that are good at multiple tasks, in various complex environments. My research studies how to build generalist robots by learning to model the world. I will show that current ideas on building generalist robots have produced powerful results such as robot face that learns to mimic human facial expressions with a self-image, robots that play hide-and-seek by predicting the opponent's visual perspective, and algorithms that can distill compact physical knowledge from video recordings of multiple dynamical systems. Future directions will enable robots to have strong flexible and adaptable behaviors, rich perception systems from multiple modalities and novel scientific discovery for dynamical system modeling and control.
Boyuan Chen is an assistant professor in the Department of Mechanical Engineering and Materials Science and the Department of Electrical and Computer Engineering. Dr. Chen obtained his Ph.D. and M.S. in Computer Science at Columbia University with Prof. Hod Lipson. He obtained his bachelor's degree in Electrical Engineering and Biomedical Engineering from Jilin University. His research focuses on robotics, computer vision, machine learning and dynamical system modeling. He is interested in developing "generalist robots" that learn, act and improve by perceiving and interacting with the complex and dynamic world. His research often relies on the natural and unlabeled sensory inputs from multiple modalities. Ultimately, he hopes that robots and machines can equip with high-level cognitive skills to assist people and unleash human creativity.