Matt Zucker

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.

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Courses taught

Publications

trajectory optimization

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

terrain scoring tool

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.

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.

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marble maze

Reinforcement Planning: RL for Optimal Planners

Matt Zucker and J. Andrew Bagnell
Proc. IEEE Int'l Conf. on Robotics and Automation, 2012.

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Note: a summary of this work appeared in The Learning Workshop at Snowbird in 2010.

block diagram

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.

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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.

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workspace biasing

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.

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MP-RRT

Multipartite RRTs for Rapid Replanning in Dynamic Environments

Matt Zucker, James Kuffner, and Michael Branicky
Proc. IEEE Int. Conf. Robotics and Automation, April, 2007.

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Asteroids

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.

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car

Approximating State-Space Obstacles for Non-Holonomic Motion Planning

Matt Zucker
tech. report CMU-RI-TR-06-27, May 2006.

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Videos

DRC-Hubo Development - 2013

Reinforcement Planning - 4/7/2010

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Demonstration of our Reinforcement Planning algorithm solving a physical simulation of the Marble Maze toy. MP4

Learning Locomotion Phase III Final - 7/14/2009

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This video demonstrates traversing a variety of rough terrains. This revision of the planning and control software is what was submitted for the final trials for Phase III of the DARPA Learning Locomotion project. MP4 | YouTube

CHOMP Trajectory Optimization - 9/15/2008

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Movie illustrating how CHOMP optimizes footstep trajectories. MP4