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Autonomous Motion Conference Paper 2011

STOMP: Stochastic trajectory optimization for motion planning

Thumb ticker sm mrinal
Autonomous Motion
Mrinal Kalakrishnan
Thumb ticker sm peter
Autonomous Motion
Peter Pastor
Thumb ticker sm ss
Autonomous Motion
Stefan Schaal
  • Director

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
Project(s):
Bibtex Type: Conference Paper (inproceedings)
Address: Shanghai, China, May 9-13
URL: http://www-clmc.usc.edu/publications/K/kalakrishnan-ICRA2011.pdf
Cross Ref: p10447
Electronic Archiving: grant_archive
Note: clmc

BibTex 

@inproceedings{Kalakrishnan_RAIIC_2011,
  title = {STOMP: Stochastic trajectory optimization for motion planning},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  abstract = {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.},
  address = {Shanghai, China, May 9-13},
  year = {2011},
  note = {clmc},
  slug = {kalakrishnan_raiic_2011},
  author = {Kalakrishnan, M. and Chitta, S. and Theodorou, E. and Pastor, P. and Schaal, S.},
  crossref = {p10447},
  url = {http://www-clmc.usc.edu/publications/K/kalakrishnan-ICRA2011.pdf}
}