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
