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Towards cognitive grasping: modeling of unknown objects and its corresponding grasp types

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Abstract

This paper describes an intuitive approach for a cognitive grasp of a robot. The cognitive grasp means the chain of processes that make a robot to learn and execute a grasping method for unknown objects like a human. In the learning step, a robot looks around a target object to estimate the 3D shape and understands the grasp type for the object through a human demonstration. In the execution step, the robot correlates an unknown object to one of known grasp types by comparing the shape similarity of the target object based on previously learned models. For this cognitive grasp, we mainly deal with two functionalities such as reconstructing an unknown 3D object and classifying the object by grasp types. In the experiment, we evaluate the performance of object classification according to the grasp types for 20 objects via human demonstration.

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References

  1. Tegin J, Ekvall S, Kragic D, Wikander J, Iliev B (2009) Demonstration-based learning and control for automatic grasping. Intel Serv Robotics 2: 23–30

    Article  Google Scholar 

  2. Mae Y, Takahashi H, Ohara K, Takubo T, Arai T (2011) Component-based robot system design for grasping tasks. Intel Serv Robotics 4: 91–98

    Article  Google Scholar 

  3. Perrin DP, Smith CE, Masoud O, Papanikolopoulos N (2000) Unknown object grasping using statistical pressure models. In: Proceedings of the IEEE international conference on robotics and automation

  4. Morales A, Recatalá G, Sanz PJ, Pobil APD (2001) Heuristic vision-based computation of planar antipodal grasps on unknown objects. In: Proceedings of the IEEE international conference on robotics and automation

  5. Taylor G, Kleeman L (2002) Grasping unknown objects with a humanoid robot. In: Australasian conference on robotics and automation

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

  7. Saxena A, Driemeyer J, Ng AY (2008) Robotic grasping of novel objects using vision. Int J Robot Res 27(2): 157–173

    Article  Google Scholar 

  8. Yamazaki K, Tomono M, Tsubouchi T, Yuta S (2004) 3-D object modeling by a camera equipped on a mobile robot. In: Proceedings of the IEEE international conference on robotics and automation

  9. Yamazaki K, Tomono M, Tsubouchi T, Yuta S (2006) A grasp planning for picking up an unknown object for a mobile manipulator. In: Proceedings of the IEEE international conference on robotics and automation

  10. Yamazaki K, Tomono M, Tsubouchi T (2008) Picking up an unknown object through autonomous modeling and grasp planning by a mobile manipulator. Springer, Berlin, p 42

    Google Scholar 

  11. Maeda Y, Ishido N, Kikuchi H, Arai T (2002) Teaching of grasp/graspless manipulation for industrial robots by human demonstration. In: Proceedings of the IEEE international conference on intelligent robots and systems

  12. Ekvall S, Kragic D (2005) Grasp recognition for programming by demonstration. In: Proceedings of the IEEE international conference on robotics and automation

  13. Ekvall S, Kragic D (2004) Interactive grasp learning based on human demonstration. In: Proceedings of the IEEE international conference on robotics and automation

  14. Hueser M, Baier T, Zhang J (2006) Learning of demonstrated grasping skills by stereoscopic tracking of human hand configuration. In: Proceedings of the IEEE international conference on robotics and automation

  15. Choi SI, Park SY, Kim J, Park YW (2008) Multi-view range image registration using CUDA. In: Proceedings of the international technical conference on circuits/systems, computers and communications

  16. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2): 91–110

    Article  Google Scholar 

  17. Yilmaz A (2006) Object tracking: a survey. ACM Comput Surv 38(4):article 13

  18. Criminisi A, Reid I, Zisserman A (1997) A plane measuring device. Image Vis Comput 17(8): 625–634

    Article  Google Scholar 

  19. Kolmogorov V, Criminisi A, Blake A, Gross G, Rother C (2005) Bi-layer segmentation of binocular stereo video. In: Proceeding conference on computer vision and pattern recognition

  20. Han I, Kim H, Kim K, Park JH (2010) Object segmentation and tracking from sequential stereo images for 3D object modeling. In: Proceedings of Korean conference on HCI Korea

  21. Tangelder JWH, Veltkamp RC (2008) A survey of content based 3D shape retrieval methods. Multimed Tools Appl 39: 441–447

    Article  Google Scholar 

  22. Biasotti S, Falcidieno B, Frosini P, Giorgi D, Landi C, Marini S, Patane G, Spagnuolo M (2007) 3D shape description and matching based on properties of real functions. In: Eurographics

  23. Turk M, Pentland A (1991) Face recognition using eigenface. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586–591

  24. Campbell R, Flynn P (1999) Eigenshapes for 3D object recognition in range data. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  25. Zhao L-W, Luo S-W, Liao L-Z (2004) 3D object recognition and pose estimation using kernel PCA. In: Proceedings of the international conference on machine learning and cybernetics

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Authors and Affiliations

  1. Interaction and Robotics Research Center, Korea Institute of Science and Technology, 39-1 Hawolgok-Dong, Seongbuk-Gu, Seoul, Korea

    Hyoungnyoun Kim, Inkyu Han, Bum-Jae You & Ji-Hyung Park

  2. Department of HCI and Robotics, University of Science and Technology, Seoul, Korea

    Hyoungnyoun Kim & Inkyu Han

Authors
  1. Hyoungnyoun Kim
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  2. Inkyu Han
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  3. Bum-Jae You
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  4. Ji-Hyung Park
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Correspondence to Ji-Hyung Park.

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Kim, H., Han, I., You, BJ. et al. Towards cognitive grasping: modeling of unknown objects and its corresponding grasp types. Intel Serv Robotics 4, 159–166 (2011). https://doi.org/10.1007/s11370-011-0088-5

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  • DOI: https://doi.org/10.1007/s11370-011-0088-5

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