Ryan Julian

Ryan Julian

Ryan Julian is a Student Researcher for the Robotics team at Google Research and a PhD student at the Robotics and Embedded Systems Laboratory, part of the Department of Computer Science at the University of Southern California. He does research at the intersection of robotics and machine learning. His PhD advisor is Gaurav Sukhatme, and from 2017 to 2018, he was also co-advised by Stefan Schaal. His advisers at Google Research are Karol Hausman and Chelsea Finn.

From 2014 to 2017, he worked at Google and X, on a series of robotics projects, including the Everyday Robot project, whose goal is to make a robot which can "learn to help everyone, every day." He worked on many parts of the robotics stack, including high-level programming APIs, 3D visualization, interprocess communication, WiFi and cloud connectivity, automatic calibration, and automation for manufacturing lines building robots. Some of the robots he helped create can be seen in the company's earliest robot learning work.

Prior to joining Google, he was a Hardware and Computer Vision Engineer at Leap Motion (now Ultraleap), where he worked on hardware, firmware, and test automation for computer vision-based hand-tracking devices, and earned a couple patents in the process. Before that, he spent a year as a Research Scientist at UC Berkeley, where he did research with—and built controllers for—some of the world's smallest intelligent robots.

Ryan has a BS in EECS from UC Berkeley, where he worked with Ron Fearing at the Biomimetic Millisystems Lab.

Authored Publications
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    Google
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
Jarek Rettinghouse
Daniel Ho
Julian Ibarz
Sangeetha Ramesh
Matt Bennice
Alexander Herzog
Chuyuan Kelly Fu
Adrian Li
Kim Kleiven
Jeff Bingham
Yevgen Chebotar
David Rendleman
Wenlong Lu
Mohi Khansari
Mrinal Kalakrishnan
Ying Xu
Noah Brown
Khem Holden
Justin Vincent
Peter Pastor Sampedro
Jessica Lin
David Dovo
Daniel Kappler
Mengyuan Yan
Sergey Levine
Jessica Lam
Jonathan Weisz
Paul Wohlhart
Karol Hausman
Cameron Lee
Bob Wei
Yao Lu
2023
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Alexander Herzog
Alexander Toshkov Toshev
Andy Zeng
Anthony Brohan
Brian Andrew Ichter
Byron David
Chelsea Finn
Clayton Tan
Diego Reyes
Dmitry Kalashnikov
Eric Victor Jang
Jarek Liam Rettinghouse
Jornell Lacanlale Quiambao
Julian Ibarz
Karol Hausman
Kyle Alan Jeffrey
Linda Luu
Mengyuan Yan
Michael Soogil Ahn
Nicolas Sievers
Noah Brown
Omar Eduardo Escareno Cortes
Peng Xu
Peter Pastor Sampedro
Rosario Jauregui Ruano
Sally Augusta Jesmonth
Sergey Levine
Steve Xu
Yao Lu
Yevgen Chebotar
Yuheng Kuang
Conference on Robot Learning (CoRL) (2022)
Actionable Models: Unsupervised Offline Learning of Robotic Skills
Benjamin Eysenbach
Chelsea Finn
Dmitry Kalashnikov
Jake Varley
Karol Hausman
Sergey Levine
Yao Lu
Yevgen Chebotar
International Conference on Machine Learning 2021 (2021)
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Benjamin Swanson
Gaurav Sukhatme
Sergey Levine
Chelsea Finn
Karol Hausman
Conference on Robot Learning, PMLR (2020)
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu
Zhanpeng He
Deirdre Quillen
Karol Hausman
Chelsea Finn
Sergey Levine
Conference on Robot Learning (2019)
Scaling simulation-to-real transfer by learning composable robot skills
Eric Heiden
Zhanpeng He
Hejia Zhang
Stefan Schaal
Joseph Lim
Gaurav Sukhatme
Karol Hausman
International Symposium on Experimental Robotics (ISER) 2018 (2018)