CHOMP: Covariant Hamiltonian optimization for motion planning

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 of...

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Bibliographic Details
Published in:The International journal of robotics research 2013-08, Vol.32 (9-10), p.1164-1193
Main Authors: Zucker, Matt, Ratliff, Nathan, Dragan, Anca D., Pivtoraiko, Mihail, Klingensmith, Matthew, Dellin, Christopher M., Bagnell, J. Andrew, Srinivasa, Siddhartha S.
Format: Article
Language:English
Subjects:
Functionally gradient materials
Invariants
Iterative methods
Kinematics
Mathematical functions
Monte Carlo methods
Monte Carlo simulation
Motion planning
Optimization
Optimization techniques
Robotics
Robots
Trajectories
Trajectory optimization
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Summary: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.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364913488805