Robotics
Having a machine learning agent interact with its environment requires true unsupervised learning, skill acquisition, active learning, exploration and reinforcement, all ingredients of human learning that are still not well understood or exploited through the supervised approaches that dominate deep learning today. Our goal is to improve robotics via machine learning, and improve machine learning via robotics. We foster close collaborations between machine learning researchers and roboticists to enable learning at scale on real and simulated robotic systems.
Recent Publications
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
Stephen Tu
Anish Shankar
Jean-Jacques Slotine
Nick Boffi
Saminda Abeyruwan
Learning for Dynamics and Control (2023)
Scalable Multi-Sensor Robot Imitation Learning via Task-Level Domain Consistency
Eric Victor Jang
Daniel Ho
Yuqing Du
Nicolas Sievers
Matt Bennice
Armando Fuentes
Mohi Khansari
ICRA (2023) (to appear)
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models
Sergey Levine
Karol Hausman
Harris Chan
Anthony Brohan
RSS 2023 (2023)
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents
Seungwon Lim
Joonhyung Lee
Youngjae Yu
Sangbeom Park
Jeongeun Park
Sungjoon Choi
IEEE Robotics and Automation Letters (2023) (to appear)
Robotic Table Tennis: A Case Study into a High Speed Learning System
Barney J. Reed
Peng Xu
Erwin Johan Coumans
Satoshi Kataoka
Corey Lynch
Navdeep Jaitly
Anish Shankar
Grace Vesom
Yuheng Kuang
Ken Oslund
Thinh Nguyen
Gus Kouretas
Saminda Abeyruwan
Juhana Kangaspunta
Justin Boyd
Michael Ahn
Wenbo Gao
Omar Escareno
Avi Singh
Jon Abelian
Robotics: Science and Systems (2023)
Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects
Huang Huang
Chung Min Kim
Ken Goldberg
Jeff Ichnowski
Michael Danielczuk
Zachary Tam
Letian Fu
Brian Ichter
The International Symposium of Robotics Research (ISRR) (2023)
Some of our teams
