#108 – Sergey Levine: Robotics and Machine Learning

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 learning, and deep RL algorithms.

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Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:
00:00– Introduction
03:05– State-of-the-art robots vs humans
16:13– Robotics may help us understand intelligence
22:49– End-to-end learning in robotics
27:01– Canonical problem in robotics
31:44– Commonsense reasoning in robotics
34:41– Can we solve robotics through learning?
44:55– What is reinforcement learning?
1:06:36– Tesla Autopilot
1:08:15– Simulation in reinforcement learning
1:13:46– Can we learn gravity from data?
1:16:03– Self-play
1:17:39– Reward functions
1:27:01– Bitter lesson by Rich Sutton
1:32:13– Advice for students interesting in AI
1:33:55– Meaning of life