Optimization-Based Control and Planning for Agile Legged Robots

Abstract: Legged robots possess a unique advantage in navigating unstructured and cluttered environments through discrete contact points, making them ideal for real-world applications such as disaster response, construction, and home assistance. However, two primary challenges persist: synthesizing dynamically feasible trajectories and bridging the reality gap between simulations and hardware implementations. My research aims to endow legged robots with the agility and decision-making capabilities akin to their biological counterparts. This talk presents two key advancements: first, a model predictive control framework that enables highly dynamic motions with large angular excursions in quadruped and humanoid robots; and second, an optimization-based trajectory generation framework that uncovers long-horizon motion strategies for traversing challenging terrains.

Bio: Dr. Yanran Ding is an Assistant Professor in the Department of Robotics at the University of Michigan. He earned his BS degree from Shanghai Jiao Tong University in 2015 and his PhD in Mechanical Engineering from the University of Illinois, Urbana-Champaign in 2021. Before joining the University of Michigan in 2023, Dr. Ding worked as a postdoctoral associate at the Massachusetts Institute of Technology’s Biomimetic Robotics Lab. His research focuses on developing agile legged robots capable of providing physical services, utilizing model-based optimization methods for motion control and planning on custom robotic platforms. He received the best student paper finalist award at IROS 2017, and a best paper finalist award at Technical Committee on model-based optimization for robotics 2021.

lab website: https://sites.google.com/umich.edu/arcad-lab/home

*** This Event will take place in a hybrid format. The location for in-person attendance will be room 1303 EECS. Attendance will also be available via Zoom.

Join Zoom Meeting: https://umich.zoom.us/j/96731875637

Meeting ID: 967 3187 5637

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Zoom Passcode information is also available upon request to Kristi Rieger([email protected])