Pete Florence: Dense Visual Representations, NeRFs, and LLMs for Robotics

In episode 54 of The Gradient Podcast, Andrey Kurenkov speaks with Pete Florence.

Note: this was recorded 2 months ago. Andrey should be getting back to putting out some episodes next year.

Pete Florence is a Research Scientist at Google Research on the Robotics at Google team inside Brain Team in Google Research. His research focuses on topics in robotics, computer vision, and natural language -- including 3D learning, self-supervised learning, and policy learning in robotics. Before Google, he finished his PhD in Computer Science at MIT with Russ Tedrake.

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Outline:

* (00:00:00) Intro

* (00:01:16) Start in AI

* (00:04:15) PhD Work with Quadcopters

* (00:08:40) Dense Visual Representations 

* (00:22:00) NeRFs for Robotics

* (00:39:00) Language Models for Robotics

* (00:57:00) Talking to Robots in Real Time

* (01:07:00) Limitations

* (01:14:00) Outro

Papers discussed:

* Aggressive quadrotor flight through cluttered environments using mixed integer programming 

* Integrated perception and control at high speed: Evaluating collision avoidance maneuvers without maps

* High-speed autonomous obstacle avoidance with pushbroom stereo

* Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation. (Best Paper Award, CoRL 2018)

* Self-Supervised Correspondence in Visuomotor Policy Learning (Best Paper Award, RA-L 2020 )

* iNeRF: Inverting Neural Radiance Fields for Pose Estimation.

* NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields.

* Reinforcement Learning with Neural Radiance Fields

* Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language.

* Inner Monologue: Embodied Reasoning through Planning with Language Models

* Code as Policies: Language Model Programs for Embodied Control