Matt Zucker

Office: Hicks 219 | | (610) 328-8636
Spring 2017 office hours: Mon 10:30AM-noon, Tue 4-5PM

I am interested in developing planning and control algorithms for complex legged robots and mobile manipulators. I believe that high-level planning, which reasons over sequences of discrete behavior primitives, is the best way to plan for such systems. My work focuses on leveraging optimization and machine learning techniques, as well as re-using previous computation, in order to produce fast planning software.

I am an associate professor in the Engineering department at Swarthmore College. Here is my CV.

You might also want to look at my coding blog.

Here is some advice for prospective graduate students, and related advice for students seeking recommendations from me.

Courses taught

Selected publications

Swarthmore student co-authors are highlighted. Please visit my Google Scholar page for a more complete listing.

cube-to-sphere example
Cube-to-sphere projections for procedural texturing and beyond
M. Zucker and Yosuke Higashi '18. Journal of Computer Graphics Techniques, 2018.
DRC-HUBO lifting heavy truss
Planning heavy lifts for humanoid robots
M.X. Grey, S. Joo, and M. Zucker. Proc. IEEE-RAS Int’l Conf. on Humanoid Robotics, 2014.
robot joint angle plots
Multigrid CHOMP with local smoothing
Keliang He '13, Elizabeth Martin '13, and Matt Zucker. Proc. IEEE-RAS Int’l Conf. on Humanoid Robotics, 2013.
CHOMP in action
CHOMP: Covariant Hamiltonian Optimization and Motion Planning
Matt Zucker, Nathan Ratliff, Anca D. Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, and Siddhartha S. Srinivasa. International Journal of Robotics Research, May 2013.
learning preferences for footstep planning
Optimization and Learning for Rough-Terrain Legged Locomotion
Matt Zucker, Nathan Ratliff, Martin Stole, Joel Chestnutt, J. Andrew Bagnell, Christopher G. Atkeson, and James Kuffner. International Journal of Robotics Research, February 2011.
robot opening door
Continuous trajectory optimization for autonomous humanoid door opening
Matt Zucker, Youngbum Jun, Brittany Killen, Tae-Goo Kim, and Paul Oh. Proc. IEEE Int'l Conf. on Technologies for Practical Robot Applications (TePRA), 2013.
marble maze
Reinforcement Planning: RL for Optimal Planners
Matt Zucker and J. Andrew Bagnell. Proc. IEEE Int'l Conf. on Robotics and Automation, 2012.
trajectory optimization
CHOMP: Gradient Optimization Techniques for Efficient Motion Planning
Nathan Ratliff, Matt Zucker, J. Andrew Bagnell, and Siddhartha Srinivasa. Proc. IEEE Int'l Conf. on Robotics and Automation, May, 2009.

Extracurricular interests

Features/Press appearances

Former students

Alums: please email me if you'd like your website to be added here!


Opening doors with the DRC-HUBO robot, using Multigrid CHOMP:

Getting Hubo walking during spring break 2013:

Robot solving a marble maze toy, June 2011

Rough terrain walking using the LittleDog quadruped robot: