Deploying legged robots in real-world applications will require fast adaptation to unknown terrain and model uncertainty. Model uncertainty could come from unknown robot dynamics, external disturbances, interaction with other humans or robots, or unknown parameters of contact models or terrain properties. In this talk, I will first present our recent works on adaptive control and adaptive safety-critical control for legged locomotion adapting to substantial model uncertainty. In these results, we focus on the application of legged robots walking rough terrain while carrying a heavy load. I will then talk about our solution on trajectory optimization that allows legged robots to adapt to a wide variety of challenging terrain. This talk will also discuss the combination of control, trajectory optimization and reinforcement learning toward achieving long-term adaptation in both control actions and trajectory planning for legged robots.

Bio:

Quan Nguyen is an Assistant Professor of Aerospace and Mechanical Engineering at the University of Southern California. Prior to joining USC, he was a Postdoctoral Associate in the Biomimetic Robotics Lab at the Massachusetts Institute of Technology (MIT). He received his Ph.D. from Carnegie Mellon University (CMU) in 2017 with the Best Dissertation Award.

His research interests span different control and optimization approaches for highly dynamic robotics including nonlinear control, trajectory optimization, real-time optimization-based control, robust and adaptive control. His work on the bipedal robot ATRIAS walking on stepping stones was featured on the IEEE Spectrum, TechCrunch, TechXplore and Digital Trends. His work on the MIT Cheetah 3 robot leaping on a desk was featured widely in many major media channels, including CNN, BBC, NBC, ABC, etc. Nguyen won the Best Presentation of the Session at the 2016 American Control Conference (ACC) and the Best System Paper Finalist at the 2017 Robotics: Science & Systems conference (RSS).

Lab website: https://sites.usc.edu/quann/