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Robotic Foundation Models

About This Talk

Understand the foundations of building general-purpose robot policies using deep learning. Sergey Levine and Liyiming Ke from Physical Intelligence explain how foundation models enable robots to handle diverse manipulation tasks and adapt across different hardware platforms. Presented at Actuate, the annual robotics developer conference by Foxglove.

Speakers

Sergey Levine, Co-Founder, Physical Intelligence

Sergey Levine is an Associate Professor at UC Berkeley and co-founder of Physical Intelligence, which focuses on developing robotic foundation models. He received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as applications in other decision-making domains. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. In 2024, he co-founded Physical Intelligence (Pi), which aims to develop a general-purpose robotic foundation model.

Liyiming Ke, Member of Technical Staff, Physical Intelligence

Liyiming Ke is a full stack robotist at Physical Intelligence researching on Machine Learning for Robot Manipulation. She earned her Ph.D. from the University of Washington with her thesis titled "Data-driven Fine Manipulation". She built a chopsticks-welding robot that demonstrate fine motor skills and developed theoretical frameworks for robot learning. She has led human-robot interactive demonstration at AAAS in 2020 and has been selected as one of the Rising Stars in EECS 2023.

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