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Demonstration-based learning and control for automatic grasping

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Abstract

We present a method for automatic grasp generation based on object shape primitives in a Programming by Demonstration framework. The system first recognizes the grasp performed by a demonstrator as well as the object it is applied on and then generates a suitable grasping strategy on the robot. We start by presenting how to model and learn grasps and map them to robot hands. We continue by performing dynamic simulation of the grasp execution with a focus on grasping objects whose pose is not perfectly known.

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Authors and Affiliations

  1. Mechatronics Laboratory, KTH Machine Design, Brinellvägen 83, 100 44, Stockholm, Sweden

    Johan Tegin & Jan Wikander

  2. Computational Vision and Active Perception Laboratory, KTH NADA/CVAP, Stockholm, Sweden

    Staffan Ekvall & Danica Kragic

  3. Center for Applied Autonomous Sensors Systems, Örebro University, Örebro, Sweden

    Boyko Iliev

Authors
  1. Johan Tegin
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  2. Staffan Ekvall
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  3. Danica Kragic
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  4. Jan Wikander
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  5. Boyko Iliev
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Correspondence to Johan Tegin.

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Tegin, J., Ekvall, S., Kragic, D. et al. Demonstration-based learning and control for automatic grasping. Intel Serv Robotics 2, 23–30 (2009). https://doi.org/10.1007/s11370-008-0026-3

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  • DOI: https://doi.org/10.1007/s11370-008-0026-3

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