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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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References

  1. Aleotti J, Caselli S, Reggiani M (2004) Leveraging on a virtual environment for robot programming by demonstration. In: Robotics and autonomous systems, special issue: robot learning from demonstration, vol 47, pp 153–161

  2. Bicchi A, Kumar V (2000) Robotic grasping and contact: a review. In: Proceedings of the IEEE international conference on robotics and automation, ICRA’00, pp 348–353

  3. Bley F, Schmirgel V, Kraiss K (2006) Mobile manipulation based on generic object knowledge. In: IEEE international symposium on robot and human interactive communication, RO-MAN’06

  4. Borst C, Fischer M, Haidacher S, Liu H, Hirzinger G (2003) DLR hand II: experiments and experiences with an antropomorphic hand. In: IEEE international conference on robotics and automation, vol 1, pp 702–707

  5. Cutkosky M (1989) On grasp choice, grasp models and the desing of hands for manufacturing tasks. IEEE Trans Robot Autom 5(3): 269–279

    Article  MathSciNet  Google Scholar 

  6. Ding D, Liu YH, Wang S (2000) Computing 3-d optimal formclosure grasps. In: Proceedings of the 2000 IEEE international conference on robotics and automation, pp 3573–3578

  7. Ekvall S, Kragic D (2005) Grasp recognition for programming by demonstration. In: IEEE/RSJ IROS

  8. Ekvall S, Kragic D (2005) Receptive field cooccurrence histograms for object detection. In: IEEE/RSJ IROS

  9. Ekvall S, Kragic D (2006) Learning task models from multiple human demonstration. In: IEEE international symposium on robot and human interactive communication, RO-MAN’06

  10. Ekvall S, Kragic D, Hoffmann F (2005) Object recognition and pose estimation using color cooccurrence histograms and geometric modeling. Image Vis Comput 23: 943–955

    Article  Google Scholar 

  11. Ferretti G, Magnani G, Rocco P, Viganò L (2006) Modelling and simulation of a gripper with dymola. Mathematical and Computer Modelling of Dynamical Systems

  12. Friedrich H, Dillmann R, Rogalla O (1999) Interactive robot programming based on human demonstration and advice. In: Christensen H et al (eds) Sensor based intelligent robots, LNAI1724, pp 96–119

  13. Howe RD (1994) Tactile sensing and control of robotic manipulation. Adv Robot 8(3): 245–261

    Article  Google Scholar 

  14. Iberall T (1997) Human prehension and dextrous robot hands. Int J Robot Res 16(3)

  15. Kragic D, Crinier S, Brunn D, Christensen HI (2003) Vision and tactile sensing for real world tasks. In: Proceedings IEEE international conference on robotics and automation, ICRA’03, vol 1, pp 1545–1550

  16. Lee MH, Nicholls HR (1999) Tactile sensing for mechatronics—a state of the art survey. Mechatronics 9(1): 1–31

    Article  MathSciNet  Google Scholar 

  17. Massimino M, Sheridan T (1989) Variable force and visual feedback effects on teleoperator man/machine performance. In: Proceedings of NASA conference on space telerobotics

  18. Miller AT, Allen P (2000) Graspit!: a versatile simulator for grasping analysis. In: Proceedings of the of the 2000 ASME international mechanical engineering congress and exposition

  19. Miller AT, Knoop S, Allen PK, Christensen HI (2003) Automatic grasp planning using shape primitives. In: Proceedings of the IEEE international conference on robotics and automation, pp 1824–1829

  20. Morales A, Chinellato E, Fagg AH, del Pobil A (2004) Using experience for assessing grasp reliability. Int J Hum Robot 1(4): 671–691

    Article  Google Scholar 

  21. Namiki A, Imai Y, Ishikawa M, Kaneko M (2003) Development of a high-speed multifingered hand system and its application to catching. In: IEEE/RSJ international conference on intelligent robots and systems, vol 3, pp 2666–2671

  22. Napier J (1956) The prehensile movements of the human hand. J Bone Joint Surg 38(4): 902–913

    Google Scholar 

  23. Platt RJ (2006) Learning and generalizing control-based grasping and manipulation skills. PhD thesis, University of Massachusetts

  24. Platt R Jr, Fagg AH, Grupen RA (2003) Extending fingertip grasping to whole body grasping. In: Proceedings of the international conference on robotics and automation

  25. Pollard NS (1994) Parallel methods for synthesizing whole-hand grasps from generalized prototypes. PhD thesis, Massachusetts Institute of Technology

  26. Pollard NS (2004) Closure and quality equivalence for efficient synthesis of grasps from examples. Int J Robot Res 23(6): 595–613

    Article  MathSciNet  Google Scholar 

  27. Skoglund A, Iliev B, Palm R (2008) A hand state approach to imitation with a next-state-planner for industrial manipulators. In: International conference on cognitive systems, Karlsruhe, Germany

  28. Tegin J, Wikander J (2004) Tactile sensing in intelligent robotic manipulation—a review. Ind Robot 32(1): 64–70

    Article  Google Scholar 

  29. Tegin J, Wikander J (2006) A framework for grasp simulation and control in domestic environments. In: IFAC-Symposium on mechatronic systems, Heidelberg, Germany

  30. Townsend W (2000) The BarrettHand grasper—programmably flexible part handling and assembly. Ind Robot Int J 27(3): 181–188

    Article  MathSciNet  Google Scholar 

  31. Volpe R, Khosla P (1993) A theorethical and experimental investigation of explicit force control strategies for manipulators. IEEE Trans Autom Control 38(11): 1634–1650

    Article  MathSciNet  Google Scholar 

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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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Corresponding author

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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