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Learning to grasp under uncertainty


Conference Paper


We present an approach that enables robots to learn motion primitives that are robust towards state estimation uncertainties. During reaching and preshaping, the robot learns to use fine manipulation strategies to maneuver the object into a pose at which closing the hand to perform the grasp is more likely to succeed. In contrast, common assumptions in grasp planning and motion planning for reaching are that these tasks can be performed independently, and that the robot has perfect knowledge of the pose of the objects in the environment. We implement our approach using Dynamic Movement Primitives and the probabilistic model-free reinforcement learning algorithm Policy Improvement with Path Integrals (PI2 ). The cost function that PI2 optimizes is a simple boolean that penalizes failed grasps. The key to acquiring robust motion primitives is to sample the actual pose of the object from a distribution that represents the state estimation uncertainty. During learning, the robot will thus optimize the chance of grasping an object from this distribution, rather than at one specific pose. In our empirical evaluation, we demonstrate how the motion primitives become more robust when grasping simple cylindrical objects, as well as more complex, non-convex objects. We also investigate how well the learned motion primitives generalize towards new object positions and other state estimation uncertainty distributions.

Author(s): Stulp, F. and Theodorou, E. and Buchli, J. and Schaal, S.
Book Title: Robotics and Automation (ICRA), 2011 IEEE International Conference on
Year: 2011

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Address: Shanghai, China, May 9-13
Cross Ref: p10445
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/stulp-ICRA2011.pdf


  title = {Learning to grasp under uncertainty},
  author = {Stulp, F. and Theodorou, E. and Buchli, J. and Schaal, S.},
  booktitle = {Robotics and Automation (ICRA), 2011 IEEE International Conference on},
  address = {Shanghai, China, May 9-13},
  year = {2011},
  note = {clmc},
  crossref = {p10445},
  url = {http://www-clmc.usc.edu/publications/S/stulp-ICRA2011.pdf}