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.

Our goal is to improve robotics via machine learning, and improve machine learning via robotics. We foster close collaborations between machine learning researchers and roboticists to enable learning at scale on real and simulated robotic systems.

We're exploring how to teach robots transferable skills, by learning in parallel across many manipulation arms in our one-of-a-kind lab purpose-built for machine learning research.

Large-scale data collection with an array of robots

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 the exciting and demanding world of self-driving cars to improve their safety and reliability.

More about this project


Robot Arm Grasping and Pushing

Multi-view Human Pouring Dataset

Featured publications

Some of our people

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