Pete Florence: Dense Visual Representations, NeRFs, and LLMs for Robotics
On how robotics can benefit from dense visual representations, neural radiance fields, and large language models
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.
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSS
Follow The Gradient on Twitter
(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
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.
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