CHOMP: Covariant Hamiltonian Optimization for Motion Planning

Matthew Zucker, Nathan Ratliff, Anca Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher Dellin, J. Andrew (Drew) Bagnell and Siddhartha Srinivasa
Journal Article, International Journal of Robotics Research, May, 2013

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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 7-DOF manipulators and a rough-terrain quadruped robot.

author = {Matthew Zucker and Nathan Ratliff and Anca Dragan and Mihail Pivtoraiko and Matthew Klingensmith and Christopher Dellin and J. Andrew (Drew) Bagnell and Siddhartha Srinivasa},
title = {CHOMP: Covariant Hamiltonian Optimization for Motion Planning},
journal = {International Journal of Robotics Research},
year = {2013},
month = {May},
keywords = {trajectory optimization, motion planning},
} 2019-07-02T13:46:03-04:00