Machine learning is a powerful tool, but it’s not a black box. At the Dynamic Robotics Lab at Oregon State University, and also at Agility Robotics, we are working to understand natural legged mobility, and then use engineering tools including machine learning to capture the same physics that underlie animal and human mobility and manipulation, and implement on machines. By capturing the same physics, we aim to achieve the same performance. This talk will focus on examples of our research applying machine learning tools to a structured control system for legged locomotion on Cassie, our bipedal robot. We’ve recently achieved walking, running, skipping and jumping, as well as transitions between the gaits; and I’ll show several videos for the first time publicly.
Jonathan W. Hurst is Chief Technology Officer and co-founder of Agility Robotics, and Professor and co-founder of the Oregon State University Robotics Institute. 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 robotic legged locomotion and physical interaction. Agility Robotics is bringing this new robotic mobility to market, solving problems for customers, working towards a day when robots can go where people go, generate greater productivity across the economy, and improve quality of life for all.