Eric Jang on Robots Learning at Google and Generalization via Language

In episode 20 of The Gradient Podcast, we talk to Eric Jang, a research scientist on the Robotics team at Google.

Eric is a research scientist on the Robotics team at Google. His research focuses on answering whether big data and small algorithms can yield unprecedented capabilities in the domain of robotics, just like the computer vision, translation, and speech revolutions before it. Specifically, he focuses on robotic manipulation and self-supervised robotic learning.

Sections:

(00:00) Intro
(00:50) Start in AI / Research
(03:58) Joining Google Robotics
(10:08) End to End Learning of Semantic Grasping
(19:11) Off Policy RL for Robotic Grasping
(29:33) Grasp2Vec
(40:50) Watch, Try, Learn Meta-Learning from Demonstrations and Rewards
(50:12) BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
(59:41) Just Ask for Generalization
(01:09:02) Data for Robotics
(01:22:10) To Understand Language is to Understand Generalization
(01:32:38) Outro

Papers discussed:

• Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

• End-to-End Learning of Semantic Grasping

• Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods

• Watch, Try, Learn Meta-Learning from Demonstrations and Rewards

• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

• Just Ask for Generalization

• To Understand Language is to Understand Generalization

• Robots Must Be Ephemeralized