Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Koopman operators in robot learning
L. Shi, M. Haseli, G. Mamakoukas, D. Bruder, I. Abraham,
T. Murphey, J. Cortés, K. Karydis
IEEE Transactions on Robotics, submitted
Abstract
Koopman operator theory offers a rigorous treatment
of dynamics and has been emerging as a powerful
modeling and learning-based control method enabling
significant advancements across various domains of
robotics. Due to its ability to represent nonlinear
dynamics as a linear operator, Koopman theory offers
a fresh lens through which to understand and tackle
the modeling and control of complex robotic
systems. Moreover, it enables incremental updates
and is computationally inexpensive making it
particularly appealing for real-time applications
and online active learning. This review
comprehensively presents recent research results on
advancing Koopman operator theory across diverse
domains of robotics, encompassing aerial, legged,
wheeled, underwater, soft, and manipulator
robotics. Furthermore, it offers practical tutorials
to help new users get started as well as a treatise
of more advanced topics leading to an outlook on
future directions and open research questions. Taken
together, these provide insights into the potential
evolution of Koopman theory as applied to the field
of robotics.
pdf
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo