We're teaching robots to predict what happens when they move objects around, in order to learn about the world around them and make better, safer decisions without supervision, and we are sharing our training data publicly to help advance the state of the art in this field. We're also bringing advances in deep learning to robot motion planning, navigation, and the exciting and demanding world of self-driving cars to improve their safety and reliability.
Having a machine learning agent interact with its environment requires true unsupervised learning, skill acquisition, active learning, exploration and reinforcement, all ingredients of human learning that are still not well understood or exploited through the supervised approaches that dominate deep learning today.