CHOMP: Covariant Hamiltonian Optimization For Motion Planning

Authors

Matthew A. Zucker, Swarthmore CollegeFollow
N. Ratliff
A. D. Dragan
M. Pivtoraiko
M. Klingensmith
C. M. Dellin
J. A. Bagnell
S. S. Srinivasa

Document Type

Article

Publication Date

8-1-2013

Published In

International Journal Of Robotics Research

Abstract

In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using seven-degree-of-freedom manipulators and a rough-terrain quadruped robot.

Comments

This work is a preprint that is freely available courtesy of SAGE Publications.

Recommended Citation

Matthew A. Zucker, N. Ratliff, A. D. Dragan, M. Pivtoraiko, M. Klingensmith, C. M. Dellin, J. A. Bagnell, and S. S. Srinivasa. (2013). "CHOMP: Covariant Hamiltonian Optimization For Motion Planning". International Journal Of Robotics Research. Volume 32, Issue 9/10. 1164-1193. DOI: 10.1177/0278364913488805
https://works.swarthmore.edu/fac-engineering/58