Sergey Levine - Building LLMs for the Physical World - [Invest Like the Best, EP.465]
Invest Like the Best with Patrick O'Shaughnessy
Data Flywheels and Sparse Sensing
Sergey Levine explains robot data collection, why usefulness matters more than dataset size, and how low-cost cameras can substitute for richer sensors.
My guest today is Sergey Levine, a professor at UC Berkeley and co-founder of Physical Intelligence. The company is building robotic foundation models designed to control any embodied system to do any task in any environment.
Sergey argues that solving robotics at full generality is the right path, and that building systems that learn across many robots, environments, and tasks may be the more scalable approach than building narrow specialists. We discuss how these models can perform new tasks without being trained on them directly, and why everyday human actions remain the hardest problems in the field.
He also reflects on how human trust and acceptance may matter as much as technical breakthroughs in determining when robots become part of daily life.
Please enjoy my conversation with Sergey Levine.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
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Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Timestamps:
(00:00:00) Welcome to Invest Like the Best
(00:02:43) Intro: Sergey Levine
(00:03:29) Why Bet on Generality Over Specialization
(00:07:24) What if PI succeeds?
(00:09:05) Pros and Cons of Humanoid Robotics
(00:11:02) Timeline of Major Milestones in Robotics
(00:15:47) Sergey's Personal Journey
(00:18:22) Making General Intelligence Happen
(00:19:57) Understanding Robot Data Collection
(00:22:12) Most Surprising Discovery at Physical Intelligence
(00:24:48) The Science of Common Sense
(00:25:36) Long-Range Tasks in Robotics
(00:27:24) Why Wouldn’t We Have A Robot in Our Kitchen by 2050
(00:31:21) Other Interesting Approaches
(00:32:38) Cool vs. Useful in Robotics
(00:36:48) Form Factor Innovation
(00:38:22) Physical Intelligence Analogy
(00:39:30) Economic Transformation from Robotics
(00:40:48) Controversies in the Robotics Community
(00:42:16) Arguments Against End-to-End Learning
(00:42:34) Compositional Learning Explained
(00:43:25) Last Tasks Robots will Conquer
(00:44:30) Dark Parts of the Robotics Brain
(00:47:05) What Makes a Great Researcher
(00:50:15) Manufacturing and Scale Challenges
(00:51:17) How Companies Should Prepare for Robotics
(00:53:38) Boston Dynamics' Demos
(00:55:43) Converging Technologies Enabling Robotics
(00:56:47) How to Stay Up To Date in Robotics
(00:59:51) Near Term Objectives
(01:00:49) Confidence Level Among Researchers
(01:03:31) Google's Experimentation Culture
(01:04:24) The Kindest Thing
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