STOMP: Stochastic trajectory optimization for motion planning

2011

Conference Paper

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We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a dual-arm mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based optimizers like CHOMP can get stuck in.

Author(s): Kalakrishnan, M. and Chitta, S. and Theodorou, E. and Pastor, P. and Schaal, S.
Book Title: IEEE International Conference on Robotics and Automation (ICRA)
Year: 2011

Department(s): Autonomous Motion
Research Project(s): Inverse Optimal Control
Bibtex Type: Conference Paper (inproceedings)

Address: Shanghai, China, May 9-13
Cross Ref: p10447
Note: clmc
URL: http://www-clmc.usc.edu/publications/K/kalakrishnan-ICRA2011.pdf

BibTex

@inproceedings{Kalakrishnan_RAIIC_2011,
  title = {STOMP: Stochastic trajectory optimization for motion planning},
  author = {Kalakrishnan, M. and Chitta, S. and Theodorou, E. and Pastor, P. and Schaal, S.},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  address = {Shanghai, China, May 9-13},
  year = {2011},
  note = {clmc},
  crossref = {p10447},
  url = {http://www-clmc.usc.edu/publications/K/kalakrishnan-ICRA2011.pdf}
}