In episode 27 of The Gradient Podcast, Andrey Kurenkov speaks to Max Braun, who leads the AI and robotics software engineering team at Everyday Robots, a moonshot to create robots that can learn to help people in their everyday lives. Previously, he worked on building frontier technology products as an entrepreneur and later at Google and X. Max enjoys exploring the intersection of art, technology, and philosophy as a writer and designer. 

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Outline:

* (00:00) Intro

* (01:00) Start in AI

* (5:45) Humanoid Research in Osaka

* (8:45) Joining Google X

* (12:15) Visual Search and Google Glass

* (15:58) Academia Industry Connection

* (18:45) Overview of Robotics Vision

* (26:00) Machine Learning for Robotics

* (32:00) Robot Platform

* (38:00) Development Process and History

* (43:35) QT-Opt

* (49:05) Imitation Learning

* (55:00) Simulation Platform

* (59:45) Sim2Real

* (1:07:00) SayCan

* (1:14:30) Current Objectives

* (1:17:00) Other Projects

* (1:21:40) Outro

Episode Links:

* Max Braun’s Website

* Everyday Robots

* Simulating Artificial Muscles for Controlling a Robotic Arm with Fluctuation

* Introducing the Everyday Robot Project

* Scalable Deep Reinforcement Learning from Robotic Manipulation (QT-Opt)

* Alphabet is putting its prototype robots to work cleaning up around Google’s offices

* Everyday robots are (slowly) leaving the lab

* Can Robots Follow Instructions for New Tasks?

* Efficiently Initializing Reinforcement Learning With Prior Policies

* Combining RL + IL at Scale

* Shortening the Sim to Real Gap

* Action-Image: Teaching Grasping in Sim

* SayCan

* I Made an AI Read Wittgenstein, Then Told It to Play Philosopher

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