Learning of grasp selection based on shape-templates

Abstract

The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.

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Notes

  1. 1.

    http://www.ros.org/wiki/pr2_tabletop_manipulation_apps/.

  2. 2.

    http://thearmrobot.com/aboutRobot.html.

  3. 3.

    http://www.ros.org.

  4. 4.

    http://www.pointclouds.org.

  5. 5.

    https://github.com/usc-clmc/usc-clmc-ros-pkg.

  6. 6.

    A short summary of the grasping experiments is also shown at http://www.youtube.com/watch?v=C7_xVxu8_RU.

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Acknowledgments

The work described in this paper was partially conducted within the EU Cognitive Systems project GRASP (IST-FP7-IP-215821) funded by the European Commission and the German Humanoid Research project SFB588 funded by the German Research Foundation (DFG: Deutsche Forschungsgemeinschaft). Alexander Herzog received support from the InterACT - International Center for Advanced Communication Technologies. This research was supported in part by National Science Foundation grants ECS-0326095, IIS-0535282, IIS-1017134, CNS-0619937, IIS-0917318, CBET-0922784, EECS-0926052, CNS-0960061, the DARPA program on Autonomous Robotic Manipulation, the Army Research Office, the Okawa Foundation, the ATR Computational Neuroscience Laboratories, and the Max-Planck-Society.

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Correspondence to Alexander Herzog.

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Herzog, A., Pastor, P., Kalakrishnan, M. et al. Learning of grasp selection based on shape-templates. Auton Robot 36, 51–65 (2014). https://doi.org/10.1007/s10514-013-9366-8

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Keywords

  • Model-free grasping
  • Grasp synthesis
  • Template learning