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
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Jesse Zhang
Jiahui Zhang
Karl Pertsch
Ziyi Liu
Xiang Ren
Shao-Hua Sun
Joseph Lim
Conference on Robot Learning 2023 (2023)
Single-Level Differentiable Contact Simulation
Simon Le Cleac'h
Mac Schwager
Zachary Manchester
Pete Florence
IEEE RAL (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)
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)
A Connection between Actor Regularization and Critic Regularization in Reinforcement Learning
Benjamin Eysenbach
Matthieu Geist
Ruslan Salakhutdinov
Sergey Levine
International Conference on Machine Learning (ICML) (2023)
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models
Harris Chan
Pierre Sermanet
Ayzaan Wahid
Anthony Brohan
Karol Hausman
Sergey Levine
Jonathan Tompson
RSS 2023 (2023)
Some of our teams
