Sergey Levine is an Associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He 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. 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 applications in other decision-making domains. 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.

Deepak Pathak is a faculty in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. from UC Berkeley and his research spans computer vision, machine learning, and robotics. He is a recipient of the faculty awards from Google, Samsung, Sony, GoodAI, and graduate fellowship awards from Facebook, NVIDIA, Snapchat. His research has been featured in popular press outlets, including The Economist, The Wall Street Journal, Quanta Magazine, Washington Post, CNET, Wired, and MIT Technology Review among others. Deepak received his Bachelor's from IIT Kanpur with a Gold Medal in Computer Science. He co-founded VisageMap Inc. later acquired by FaceFirst Inc.

Karen Liu is a professor in the Computer Science Department at Stanford University. Prior to joining Stanford, Liu was a faculty member at the School of Interactive Computing at Georgia Tech. She received her Ph.D. degree in Computer Science from the University of Washington. Liu's research interests are in computer graphics and robotics, including physics-based animation, character animation, optimal control, reinforcement learning, and computational biomechanics. She developed computational approaches to modeling realistic and natural human movements, learning complex control policies for humanoids and assistive robots, and advancing fundamental numerical simulation and optimal control algorithms. The algorithms and software developed in her lab have fostered interdisciplinary collaboration with researchers in robotics, computer graphics, mechanical engineering, biomechanics, neuroscience, and biology. Liu received a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and was named Young Innovators Under 35 by Technology Review. In 2012, Liu received the ACM SIGGRAPH Significant New Researcher Award for her contribution in the field of computer graphics.

Berthold Bauml is head of the Autonomous Learning Robots Lab. His longterm goal in this basic research lab is to use learning as the core principle in building really autonomous robots which can operate in complex and changing environments. A prerequisite for this research program is robotic hardware which comes close to human performance with respect to sensor and motor capabilities and has a realtime connection to large and scalable computing resources. In the last years we upgraded one of our torque controlled humanoid robots to our award winning research platform Agile Justin [2] which, among others, now has realtime 3D environment modeling, high resolution spatio-temporal tactile sensing on the whole body and the hands and a wirelessly coupled GPU cluster in-the-loop.

Zhengyi Luo is a final year PhD student at Carnegie Mellon University’s Robotics Institute, School of Computer Science, advised by Prof. Kris Kitani. He earned his bachelor’s degree from University of Pennsylvania in 2019, where he worked with Prof. Kostas Daniilidis. His research has been supported by Qualcomm Innovation Fellowship and the Meta AI Mentorship Program. His research interest lies at the intersection of vision, learning, and robotics. He is working on topcis including human pose estimation, human-object interaction, human motion modelling etc. Through his research, he wants to create methods that effectively interpret spatial-temporal sensory input and build a representation of the 3D world to reason about the interactions between agents and the physical environment. On the application side, he is excited about humanoid robots and AR/VR.

Eric Jang leads the AI team at 1X Technologies, a vertically-integrated humanoid robot company. His research background is on end-to-end mobile manipulation and generative models. Eric recently authored a book on the future of AI and Robotics, titled "AI is Good for You".

Alberto Rodriguez is currently a Research Scientist, Atlas Manipulation Lead at Boston Dynamics. Alberto Rodriguez brings experience from previous roles at Massachusetts Institute of Technology. Alberto Rodriguez holds a 2007 - 2013 Doctor of Philosophy - PhD in Robotics @ Carnegie Mellon University.

Hang Zhao is an Assistant Professor at IIIS, Tsinghua University, Principle Investigator of MARS Lab. His research interests are multi-modal machine learning, autonomous driving and robot learning. He was a Research Scientist at Waymo (known as Google's self-driving project) from 2019 to 2020. Before that, he got his Ph.D. degree at MIT in 2019 under the supervision of Professor Antonio Torralba. Before MIT, he received his B.S. from Zhejiang University in 2013.

Corey Lynch is a research scientist at Figure AI.

Marc Raibert is the Executive Director of the Boston Dynamics AI Institute, a Hyundai Motor Group organization. Raibert was the founder, former CEO, and now Chairman of Boston Dynamics, a robotics company known for creating BigDog, Atlas, Spot, and Handle.