While legged robots have made remarkable progress in dynamic balancing and mobility, there remains substantial room for improvement in terms of navigation and decision-making capabilities. One major challenge stems from the difficulty of designing safe, resilient, and real-time planning and decision-making frameworks for these complex legged machines navigating unstructured environments. Symbolic planning and distributed trajectory optimization offer promising yet underexplored solutions. This talk will introduce three perspectives on enhancing safety and resilience in task and motion planning (TAMP) for agile legged locomotion. First, we'll discuss hierarchically integrated TAMP for dynamic locomotion in environments susceptible to perturbations, focusing on robust recovery behaviors. Next, we'll cover our recent work on safe and socially acceptable legged navigation planning in environments that are partially observable and crowded with humans. Lastly, we'll delve into distributed contact-aware trajectory optimization methods achieving dynamic consensus for agile locomotion behaviors.

Bio: 

Ye Zhao is an Assistant Professor at The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. He received his Ph.D. degree in Mechanical Engineering from The University of Texas at Austin in 2016. After that, he was a Postdoctoral Fellow at Agile Robotics Lab, Harvard University. At Georgia Tech, he leads the Laboratory for Intelligent Decision and Autonomous Robots. His research interest focuses on planning and decision-making algorithms of highly dynamic and contact-rich robots. He received the George W. Woodruff School Faculty Research Award at Georgia Tech in 2023, NSF CAREER Award in 2022, and ONR Young Investigator Program Award in 2023. He serves as an Associate Editor of IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, and IEEE Control Systems Letters. His co-authored work has received multiple paper awards, including the 2021 ICRA Best Automation Paper Award Finalist and the 2016 IEEE-RAS Whole-Body Control Best Paper Award Finalist.