Matt Zucker
Office: Hicks 219 |
(610) 328-8636 |
mzucker1@swarthmore.edu
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.
Here is my CV.
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Courses taught
- ENGR 91 - Humanoid Robotics - Spring 2013
- ENGR 027 - Computer Vision - Spring 2013, Spring 2012, Spring 2011
- ENGR 019 - Numerical Methods for Engineering - Fall 2012
- ENGR 028 - Mobile Robotics - Fall 2012
- ENGR 012 - Linear Physical Systems Analysis Labs - Spring 2012, Spring 2011
- ENGR 015 - Fundamentals of Digital Systems - Fall 2011 (labs), Fall 2010
Publications
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, accepted for publication in April 2013
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, 30(2):175-191, February 2011.
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.
Reinforcement Planning: RL for Optimal Planners
Matt Zucker and J. Andrew Bagnell
Proc. IEEE Int'l Conf. on Robotics and Automation, 2012.
Note: a summary of this work appeared in The Learning Workshop at Snowbird in 2010.
An Optimization Approach to Rough Terrain Locomotion
Matt Zucker, J. Andrew Bagnell, Christopher G. Atkeson, and James Kuffner
Proc. IEEE Int'l Conf. on Robotics and Automation, May,
2010.
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.
Adaptive Workspace Biasing for Sampling Based Planners
Matt Zucker, James Kuffner, and J. Andrew Bagnell
Proc. IEEE Int'l Conf. on Robotics and Automation, May,
2008.
Multipartite RRTs for Rapid Replanning in Dynamic Environments
Matt Zucker, James Kuffner, and Michael Branicky
Proc. IEEE Int. Conf. Robotics and Automation, April, 2007.
Improved Motion Planning Speed and Safety using Regions of Inevitable Collision
Nicholas Chan, James Kuffner, and Matt Zucker
17th CISM-IFToMM Symposium on Robot Design, Dynamics, and
Control (RoManSy'08), July, 2008.
Approximating State-Space Obstacles for Non-Holonomic Motion Planning
Matt Zucker
tech. report CMU-RI-TR-06-27, May 2006.