Abstract:  Recent advances in motion planning and control have led to dramatic successes like SpaceX’s rocket landings and Boston Dynamics’ humanoid robot acrobatics. However, the underlying numerical methods used in these applications are typically decades old, not tuned for high performance on planning and control problems, and are often unable to cope with the types of optimization problems that arise naturally in modern robotics applications like legged locomotion and autonomous driving. This talk will introduce new numerical optimization tools being built to enable robotic systems that move with the same agility, efficiency, and safety as humans and animals. Some target applications include legged locomotion; autonomous driving; distributed control of satellite swarms; and spacecraft entry, descent, and landing. I will also discuss hardware platforms being developed to test real-time embedded implementations of key algorithms in challenging environments, including quadrupeds, teams of quadrotors, and tiny satellites.

Bio:  Zac Manchester is an Assistant Professor of Aeronautics and Astronautics at Stanford University, founder of the KickSat project, and member of the Breakthrough Starshot Advisory Committee. 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 previously worked at NASA Ames Research Center and Analytical Graphics, Inc. He received a NASA Early Career Faculty Award in 2018 and has led three satellite missions. His research interests include motion planning, control, and numerical optimization, particularly with application to robotic locomotion and small spacecraft.