Abstract:

Recent advances in motion planning and control have led to dramatic successes like SpaceX’s autonomous rocket landings and Boston Dynamics’ humanoid robot acrobatics. However, the underlying numerical methods used in these applications are typically decades old, are not tuned for high performance on control problems, and are often unable to cope with the types of optimization problems that arise naturally in modern robotics applications like locomotion, manipulation, and autonomous driving. This talk will focus on numerical optimization tools built explicitly to solve such challenging motion planning and control problems at real-time rates to enable robotic systems that move with the same agility, efficiency, and safety as humans and animals. I’ll also highlight recent applications including humanoid robots, quantum computers, and satellite swarms.

Bio:

Zac Manchester is an Assistant Professor of Robotics at Carnegie Mellon University. He holds a Ph.D. in aerospace engineering and a B.S. in applied physics from Cornell University. Zac was a postdoc in the Agile Robotics Lab at Harvard University and has previously worked at Stanford, NASA Ames Research Center, and Analytical Graphics, Inc. He received a NASA Early Career Faculty Award in 2018 and a Google Faculty Research Award in 2019, and he has led four NASA-funded satellite missions. His research interests include control, motion planning, and numerical optimization, particularly with application to robotic locomotion and spacecraft guidance, navigation, and control.