Leslie Pack Kaelbling is Professor of Computer Science and Engineering at MIT. She has previously held positions at Brown University, the Artificial Intelligence Center of SRI International, and at Teleos Research. Prof. Kaelbling has done substantial research on designing situated agents, mobile robotics, reinforcement learning, and decision-theoretic planning. In 2000, she founded the Journal of Machine Learning Research, a high-quality journal that is both freely available electronically as well as published in archival form; she currently serves as editor-in-chief. She is an NSF Presidential Faculty Fellow, a former member of the AAAI Executive Council, the 1997 recipient of the IJCAI Computers and Thought Award, a trustee of IJCAII and a fellow of the AAAI. She received an A. B. in Philosophy in 1983 and a Ph. D. in Computer Science in 1990, both from Stanford University.

Martin Riedmiller is a research scientist at DeepMind. He was previously a professor at the University of Freiberg in Germany, working on robotics and autonomous learning systems, were he led the multiple-time world champion RoboCup team. His general research interest is to investigate and develop 'intelligent' software modules by applying machine learning techniques to interesting real world problems. His main research activities are in the areas of supervised learning (learning from examples) and reinforcement learning (autonomous learning without a teacher).

Marc Toussaint is full professor for Machine Learning and Robotics at the University of Stuttgart since 2012. Before he was assistant professor at the Free University Berlin, leading an Emmy Noether research group at TU Berlin, and spent two years as a post-doc at the University of Edinburgh. His research focuses on the combination of decision theory and machine learning, motivated by fundamental research questions in robotics. Reoccurring themes in his research are appropriate representations (symbols, temporal abstractions, relational representations) to enable efficient learning and manipulation in real world environments, and how to achieve jointly geometric, logic and probabilistic learning and reasoning. He currently is coordinator of the German research priority programme on Autonomous Learning, member of the editorial board of the Journal of AI Research (JAIR), reviewer for the German Research Foundation, and programme committee member of several top conferences in the field (UAI, R:SS, ICRA, IROS, AIStats, ICML). His work was awarded best paper at R:SS’12, ICMLA’07 and runner up at UAI’08.

Igor Mordatch is a senior researcher at OpenAI. He was previously a post-doctoral fellow working with professor Pieter Abbeel at University of California, Berkeley. He received his PhD at University of Washington under supervision of Emanuel Todorov and Zoran Popovic and undergraduate degree in Computer Science and Mathematics at University of Toronto. He worked as a visiting researcher at Stanford University and Pixar Research. His research interests lie in the development and use of optimal control and machine learning techniques for robotics, computer graphics, and biomechanics.

Roy Fox is a postdoc at UC Berkeley working with Ion Stoica in the Real-Time Intelligent Secure Explainable Systems Lab (RISELab) and with Ken Goldberg in the Laboratory for Automation Science and Engineering (AUTOLAB). His research interests include reinforcement learning, dynamical systems, information theory, robotics, and the connections between these fields. His current research focuses on automatic discovery of hierarchical control structures in deep reinforcement learning and imitation learning of robotic tasks. Roy holds a MSc in Computer Science from the Technion, under the supervision of Moshe Tennenholtz, and a PhD in Computer Science from the Hebrew University, under the supervision of Naftali Tishby. He was an exchange PhD student with Larry Abbott and Liam Paninski at the Center for Theoretical Neuroscience at Columbia University, and a research intern at Microsoft Research.

Tuomas Haarnoja is a Ph.D. student in EECS at UC Berkeley focusing on deep learning and robotics. In particular, his work aims to incorporate machine vision into control of UAVs to enable autonomous flight in unstructured and dynamic environments where GPS signal is not available or is irrelevant for the task at hand. Haarnoja holds a master’s of science degree in Space Robotics and Automation from Luleå University of Technology, Sweden, and BSc degree in Automation and Systems Technology from Helsinki University of Technology (now Aalto University), Finland. Before coming to Berkeley, he worked as a research scientist at VTT Technical Research Centre of Finland, where he primarily contributed to the research of magnetically levitated electric machines. He collaborates with Pieter Abbeel on Berkeley DeepDrive project.

Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. HHe does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded Conviva, a startup to commercialize technologies for large scale video distribution.

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 the CITRIS "People and Robots" Initiative 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 250 peer-reviewed publications and 8 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.

Pieter Abbeel (BS/MS EE KU Leuven, 2000; PhD CS Stanford, 2008, Advisor: Andrew Ng) is professor at UC Berkeley (EECS, BAIR) since 2008 and is a Research Scientist at OpenAI since 2016. Pieter has developed apprenticeship learning algorithms which have enabled advanced helicopter aerobatics, including maneuvers such as tic-tocs, chaos and auto-rotation, which only exceptional human pilots can perform. His group has enabled the first end-to-end completion of reliably picking up a crumpled laundry article and folding it and has pioneered deep reinforcement learning for robotics, including learning locomotion and visuomotor skills. His work has been featured in many popular press outlets, including BBC, New York Times, MIT Technology Review, Discovery Channel, SmartPlanet and Wired. His current research focuses on robotics and machine learning with particular focus on deep reinforcement learning, deep imitation learning, deep unsupervised learning, and AI safety. Pieter has won various awards, including the Sloan Research Fellowship, the Air Force Office of Scientific Research Young Investigator Program (AFOSR-YIP) award, the Okawa Research Grant, the 2011 TR35, the IEEE Robotics and Automation Society (RAS) Early Career Award, and the Dick Volz Best U.S. Ph.D. Thesis in Robotics and Automation Award. Pieter was awarded the NSF PECASE (Presidential Early Career Awards for Scientists and Engineers) from President Barack Obama in 2016.

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.