Join host Craig Smith on episode #176 of Eye on AI as he dives deep into the realm of robotic artificial intelligence with Sergey Levine, associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. 
In this episode, Sergey unveils the latest advancements in AI control of robots, exploring the implications of reinforcement learning and the concept of embodied AI. 
Discover how Sergey's research is pushing the boundaries of AI, enabling robots to learn manipulation skills and generalize across diverse tasks, transforming the potential of home robots and beyond.
Sergey also shares insights into the RTX project, an ambitious collaboration designed to achieve remarkable generalization across different robot morphologies, enhancing robots' ability to perform language-conditioned manipulation tasks.
If you're fascinated by the intersection of AI, robotics, and the quest for creating adaptable, generalizable machines that promise to revolutionize our interaction with technology, this episode is a must-listen. 
Remember to rate us on Apple Podcast and Spotify if this episode ignites your interest in the dynamic field of robotic AI and the visionary work of Sergey Levine.

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(00:00) Preview and Introduction to Home Robots and AI in Robotics
(01:43) World Models and Language Models in Robotics
(04:01) The Challenge of Learning-Based Control and Data Utilization
(06:05) RTX Project: Generalizing Controllers Across Different Robots
(10:09) Uniformity in Model Architecture Across Labs
(13:50) Introduction of RT1 and RT2 Models for Robot Control
(16:06) The Future of Robotic Control Research and Architecture
(18:49) The Impact of Hardware Development on Robotics
(22:15) Advances in Controller and AI Model Development
(26:21) Planning and Acting with Vision Language Models
(31:38) The Proprietary vs. Open-Source Debate in Robotics
(36:23) The Future of Commercial and Open Source Robotics Applications
(40:59) The State of Robotics Research in China