CHOMP: Covariant Hamiltonian optimization for motion planning

  • Authors:

  • Matt Zucker;Nathan Ratliff;Anca D. Dragan;Mihail Pivtoraiko;Matthew Klingensmith;Christopher M. Dellin;J. Andrew Bagnell;Siddhartha S. Srinivasa

  • Affiliations:

  • Department of Engineering, Swarthmore College, Swarthmore, PA, USA;Google, Inc., Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA;Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2013

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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.