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
Saminda Abeyruwan
Nick Boffi
Anish Shankar
Jean-Jacques Slotine
Stephen Tu
Learning for Dynamics and Control (2023)
Scalable Multi-Sensor Robot Imitation Learning via Task-Level Domain Consistency
Armando Fuentes
Daniel Ho
Eric Victor Jang
Matt Bennice
Mohi Khansari
Nicolas Sievers
Yuqing Du
ICRA (2023) (to appear)
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models
Harris Chan
Anthony Brohan
Karol Hausman
Sergey Levine
RSS 2023 (2023)
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents
Jeongeun Park
Seungwon Lim
Joonhyung Lee
Sangbeom Park
Sungjoon Choi
Youngjae Yu
IEEE Robotics and Automation Letters (2023) (to appear)
Robotic Table Tennis: A Case Study into a High Speed Learning System
Jon Abelian
Saminda Abeyruwan
Michael Ahn
Justin Boyd
Erwin Johan Coumans
Omar Escareno
Wenbo Gao
Navdeep Jaitly
Juhana Kangaspunta
Satoshi Kataoka
Gus Kouretas
Yuheng Kuang
Corey Lynch
Thinh Nguyen
Ken Oslund
Barney J. Reed
Anish Shankar
Avi Singh
Grace Vesom
Peng Xu
Robotics: Science and Systems (2023)
Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects
Huang Huang
Letian Fu
Michael Danielczuk
Chung Min Kim
Zachary Tam
Jeff Ichnowski
Brian Ichter
Ken Goldberg
The International Symposium of Robotics Research (ISRR) (2023)
Some of our teams
