The 2017 Symposium on Robot Learning (SoRL) is dedicated to the memory of Prof. Hubert Dreyfus who passed away on 22 April 2017
Watch a recording of the event >
The Symposium on Robot Learning (SoRL) is a one-day symposium that will focus on emerging advances at the intersection of Robotics and Machine Learning, which is experiencing a surge in research activity using expressive learning methods such as deep learning.
The symposium will consider a range of approaches and applications with long and short presentations and posters, to identify research themes, open questions, and set the stage for the 3-day Conference on Robot Learning (CoRL), a new annual meeting that will launch in November.
11:00: Welcome – Professor Ken Goldberg & Sergey Levine, UC Berkeley
11:15: Keynote Speaker – Vincent Vanhoucke, Google Brain
12:00: Keynote Speaker – Emo Todorov, University of Washington
12:45: Lunch and breakout sessions
02:00: Keynote Speaker – Chelsea Finn, UC Berkeley
02:45: Lightning Presentations – Student Groups, UC Berkeley
03:15: Poster Session with Coffee Break
04:15: Keynote Speaker – Wojciech Zaremba, OpenAI
Other Speakers include Fereshteh Sadeghi, Claire Tomlin, Pieter Abbeel, Anca Dragan, Gabe Elkaim, Ricardo Sanfelice, and Stavros Vougioukas.
Watch the recording of the Symposium on Robot Learning SoRL 2017 >
Principal Scientist | Google Brain
Vincent Vanhoucke is a Principal Scientist at Google. He is a technical lead in the Google Brain Team and manages Google’s Robotics Research effort. Prior to that, he leads Brain’s vision and perception research, and the speech recognition quality team for Google Search by Voice. He holds a Ph.D. in Electrical Engineering from Stanford University and a Diplôme d’Ingénieur from the Ecole Centrale Paris.
Researcher and Founder | OpenAI
Wojciech Zaremba is a researcher/founder at OpenAI. He completed his Ph.D. at New York University. He has been awarded Google Fellowship in 2015-2016.
He used to work at Facebook AI Research (FAIR) under the supervision of Prof. Rob Fergus and Prof. Yann LeCun. Moreover, he has been interning multiple times at Google Brain (Google Brain) with Prof. Geoffrey Hinton, and Ilya Sutskever. Together with his team at Google, they developed deep learning-based photo search for Google+. A few years back, he used to work at Nvidia on GPU driver memory management, Center for Learning and Visual Computing at École Centrale, and Digital Enterprise Research Institute at the National University of Ireland.
Computer Science Ph.D. Student | UC Berkeley
Chelsea Finn is a Ph.D. student in CS at UC Berkeley, where she works on machine learning for robotic perception and control. She is a part of Berkeley AI Research Lab (BAIR), advised by Pieter Abbeel and Sergey Levine. She recently spent time at Google Brain.
Before graduate school, she received a Bachelors in EECS at MIT, where she worked on several research projects, including an assistive technology project in CSAIL under Seth Teller and an animal biometrics project under Sai Ravela. She has also spent time at Counsyl, Google, and Sandia National Labs.
Associate Professor, Computer Science & Engineering | University of Washington
Emanuel Todorov, Associate Professor, graduated from MIT in 1998 with a Ph.D. in Cognitive Neuroscience. He joined the University of Washington from the Department of Cognitive Science at the University of California San Diego.
The real-time control of a complex musculo-skeletal system such as the human body requires the generation of thousands of control signals per second. Humans’ ability to accomplish difficult tasks – in the face of noise, delays, uncertainty, and constantly changing circumstances – suggests that these control signals are chosen rather intelligently and to a large extent online. Todorov’s research focus is intelligent control in biological and artificial systems. He combines experimental and computational methods to investigate how the brain controls the body in a variety of motor tasks. He also develops biologically-inspired control algorithms that aim to solve complex problems beyond the reach of traditional control theory.
Professor, Electrical Engineering and Computer Sciences | UC Berkeley
Ken Goldberg is an artist, inventor, and UC Berkeley Professor. He is Chair of the Industrial Engineering and Operations Research Department, with secondary appointments in EECS, Art Practice, the School of Information, and Radiation Oncology at the UCSF Medical School. Ken is Director of CITRIS People and Robots and the UC Berkeley AUTOLAB where he and his students pursue research in geometric algorithms and machine learning for robotics and automation in surgery, manufacturing, and other applications. Ken developed the first provably complete algorithms for part feeding and part fixturing and the first robot on the Internet. Despite agonizingly slow progress, Ken persists in trying to make robots less clumsy. He has over 200 peer-reviewed publications and eight U.S. Patents. He co-founded and served as Editor-in-Chief of the IEEE Transactions on Automation Science and Engineering. Ken’s artwork has appeared in 70 exhibits including the Whitney Biennial and films he has co-written have been selected for Sundance and nominated for an Emmy Award. Ken was awarded the NSF PECASE (Presidential Faculty Fellowship) from President Bill Clinton in 1995, elected IEEE Fellow in 2005, and selected by the IEEE Robotics and Automation Society for the George Saridis Leadership Award in 2016. He lives in the Bay Area and is madly in love with his wife, filmmaker and Webby Awards founder Tiffany Shlain, and their two daughters. He is fiercely protective of his family, his students, and his frequent-flier miles. (goldberg.berkeley.edu @Ken_Goldberg)
Assistant Professor, Electrical Engineering and Computer Sciences | UC Berkeley
Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. His work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business.