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Sergey Levine's Interviews
π0: A Foundation Model for Robotics with Sergey Levine
Today, we're joined by Sergey Levine, associate professor at UC Berkeley and co-founder of Physical Intelligence, to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the model architecture, which pairs a vision language model (VLM) with a diffusion-based action expert, and the model trainin
Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems
Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In this episode, we talk about the bottlenecks to generalization in reinforcemen
AI Trends 2023: Reinforcement Learning - RLHF, Robotic Pre-Training, and Offline RL with Sergey Levine
Today we’re taking a deep dive into the latest and greatest in the world of Reinforcement Learning with our friend Sergey Levine, an associate professor, at UC Berkeley. In our conversation with Sergey, we explore some game-changing developments in the field including the release of ChatGPT and the onset of RLHF. We al
Sergey Levine explains the challenges of real world robotics
In Episode One of Season Two, Host Pieter Abbeel is joined by guest (and close collaborator) Sergey Levine, professor at UC Berkeley, EECS. Sergey discusses the early years of his career, how Andrew Ng influenced him to become interested in machine learning, his current projects, and his lab's recent accomplishments. T
Sergey Levine: "Is Reinforcement Learning Sufficient For Designing Generic And Adaptable Robots?"
Sergey Levine: "Is Reinforcement Learning Sufficient For Designing Generic And Adaptable Robots?" Video format: https://youtu.be/UyOGMqMYarE
Sergey Levine is a professor at Berkeley and a world-class researcher in deep learning, reinforcement learning, robotics, and computer vision, including the development of algorithms for end-to-end training of neural network policies that combine perception and control, scalable algorithms for inverse reinforcement lea
Podcasts with Sergey Levine
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