Michiel Van de Panne, UBC
Michiel van de Panne is a Professor of Computer Science at the University of British Columbia. His research group develops physics-based models of human and animal movement, and pursues problems related to motion planning, robotics, and machine learning methods including deep reinforcement learning. He cofounded the ACM SIGGRAPH/Eurographics Symposium on Computer Animation and serves as an Associate Editor of ACM Transactions on Graphics. His students have won numerous best paper awards and thesis awards, and have gone on to play key roles at Tesla, DeepMotion, Element AI, Electronic Arts, Anomotion, ETH Zurich and SFU, among others. He is the recipient of the 2016 CHCCS Achievement award for contributions to computer graphics in Canada.
Jonathan Hurst, Agility Robotics
Jonathan W. Hurst is a Professor of Robotics, co-founder of the Oregon State University Robotics Institute, and Chief Technology Officer and co-founder of Agility Robotics. He holds a B.S. in mechanical engineering and an M.S. and Ph.D. in robotics, all from Carnegie Mellon University. His university research focuses on understanding the fundamental science and engineering best practices for legged locomotion. Investigations range from numerical studies and analysis of animal data, to simulation studies of theoretical models, to designing, constructing, and experimenting with legged robots for walking and running, and more recently, using machine learning techniques merged with more traditional control to enable highly dynamic gaits.
Patrick Wensing, University of Notre Dame
Patrick Wensing is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame where he directs the Robotics, Optimization, and Assistive Mobility (ROAM) lab. He received his Ph.D. in Electrical and Computer Engineering from The Ohio State University in 2014, and completed Postdoctoral training at MIT in 2017 where he worked on control system design for the MIT Cheetah robots. His current research focuses on aspects of dynamics, optimization, and control toward advancing the mobility of legged robots and assistive devices.
Jemin Hwangbo, KAIST
Jemin Hwangbo is an assistant professor at the Korean Advanced Institute of Science and Technology (KAIST). Prir to that, he did his PhD at ETH working on applying Reinforcement Learning on the quadruped robot Anymal. His main research interest is learning-based control strategies for multi-body systems (nonlinear and non-smooth hybrid systems) using deep neural networks.
Daniel Holden, Ubisoft
Daniel Holden is a Machine Learning and Animation Researcher at Ubisoft Montreal's La Forge research lab. Before joining Ubisoft he completed his PhD at The University of Edinburgh under supervisor Taku Komura with publications in several esteemed conferences such as SIGGRAPH and SIGGRAPH Asia. His research focuses on how Machine Learning andDeep Neural Networks can be applied to many practical problems in game development, with a specific focus on animation and character control.
Nicolas Heess, DeepMind
Nicolas Heess is a Research Scientist at DeepMind, London. He is interested in questions related to artificial intelligence and machine learning, perception, motor control, and robotics. He has worked on the theory and applications of reinforcement learning, unsupervised learning, probabilistic models, and inference. One of his long-term goals is to develop algorithms and architectures that enable embodied agents to learn to intelligently reason about and interact with their environment, and with other agents. Prior to joining DeepMind Nicolas was a postdoctoral researcher at the Gatsby Unit (UCL) working with Yee Whye Teh and David Silver and completed his PhD under the supervision of Chris Williams at the University of Edinburgh.
Gerardo Bledt, MIT
Gerardo Bledt is the Lead Locomotion Engineer at Apptronik researching control algorithms for robust, dynamic legged robots in useful, real-world applications. Before joining Apptronik, he was a Postdoctoral Researcher at the MIT Biomimetic Robotics lab working on Reinforcement Learning and humanoid robot locomotion. He received his Ph.D. in Robotics at the same lab developing Regularized Predictive Control (RPC), a data-driven, nonlinear optimization-based predictive controller, which was implemented on the MIT Cheetah 3 and Mini Cheetah robots. He also completed Masters degrees in Electrical Engineering & Computer Science and Mechanical Engineering at MIT, as well as a Bachelors degree in Mechanical Engineering at Virginia Tech.
Majid Khadiv, MPI
Majid Khadiv is a postdoctoral researcher in the Movement Generation and Control Group at the Max Planck Institute for Intelligent Systems (MPI-IS). During his PhD, he participated in the Surena III humanoid robot project as the lead control developer. He also spent one year of his PhD as a research visitor at the Autonomous Motion Department (AMD) at MPI-IS. His main research interest is control of robotics systems, especially legged robots.