The Visual Computer

, Volume 34, Issue 2, pp 257–270 | Cite as

Grasp planning via hand-object geometric fitting

Original Article


Grasp planning is crucial for many robotic applications such as object manipulation and object transport. Planning stable grasps is a challenging problem. Many parameters such as object geometry, hand geometry and kinematics, hand-object contacts have to be considered, making the space of grasps too large to be exhaustively searched. This paper presents a general approach for planning grasps on 3D objects based on hand-object geometric fitting. Our key idea is to build a contact score map on a 3D object’s voxelization, and apply this score map and a hand’s kinematic parameters to find a set of target contacts on the object surface. Guided by these target contacts, we find grasps with a high quality measure by iteratively adjusting the hand pose and joint angles to fit the hand’s instantaneous geometric shape with the object’s fixed shape, during which the fitting process is speeded up by taking advantage of the discrete volumetric space. We demonstrate the effectiveness of our grasp planning approach on 3D objects of various shapes, poses, and sizes, as well as hand models with different kinematics. A comparison with two state-of-the-art approaches shows that our approach can generate grasps that are more likely to be stable, especially for objects with complex shapes.


Grasp planning 3D object Hand model Geometric fitting Grasp quality 



This work is supported in part by Anhui Provincial Natural Science Foundation (1508085QF122), National Natural Science Foundation of China (61403357, 61672482, 11526212), and the One Hundred Talent Project of the Chinese Academy of Sciences.

Supplementary material

371_2016_1333_MOESM1_ESM.mp4 (49.4 mb)


  1. 1.
    Amor, H.B., Heumer, G., Jung, B., Vitzthum, A.: Grasp synthesis from low-dimensional probabilistic grasp models. Comput. Anim. Virtual Worlds 19(3–4), 445–454 (2008)CrossRefGoogle Scholar
  2. 2.
    Attene, M., Falcidieno, B., Spagnuolo, M.: Hierarchical mesh segmentation based on fitting primitives. Vis. Comput. 22(3), 181–193 (2006)CrossRefGoogle Scholar
  3. 3.
    Aydin, Y., Nakajima, M.: Database guided computer animation of human grasping using forward and inverse kinematics. Comput. Graph. 23(1), 145–154 (1999)CrossRefGoogle Scholar
  4. 4.
    Berenson, D., Diankov, R., Nishiwaki, K., Kagami, S., Kuffner, J.: Grasp planning in complex scenes. In: IEEE-RAS International Conference on Humanoid Robots, pp. 42–48 (2007)Google Scholar
  5. 5.
    Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis—a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)CrossRefGoogle Scholar
  6. 6.
    Chalmeta, R., Hurtado, F., Sacristán, V., Saumell, M.: Measuring regularity of convex polygons. Comput. Aided Des. 45(2), 93–104 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Ciocarlie, M.T., Allen, P.K.: Hand posture subspaces for dexterous robotic grasping. Int. J. Robot. Res. 28(7), 851–867 (2009)CrossRefGoogle Scholar
  8. 8.
    Ding, L., Ding, X., Fang, C.: 3D face sparse reconstruction based on local linear fitting. Vis. Comput. 30(2), 189–200 (2014)CrossRefGoogle Scholar
  9. 9.
    Goldfeder, C., Allen, P.K., Lackner, C., Pelossof, R.: Grasp planning via decomposition trees. In: IEEE International Conference on Robotics and Automation, pp. 4679–4684 (2007)Google Scholar
  10. 10.
    Güngör, C., Kurt, M.: Improving visual perception of augmented reality on mobile devices with 3D red–cyan glasses. In: the IEEE 22nd Signal Processing and Communications Applications Conference, pp. 1706–1709 (2014)Google Scholar
  11. 11.
    Huebner, K., Ruthotto, S., Kragic, D.: Minimum volume bounding box decomposition for shape approximation in robot grasping. In: IEEE International Conference on Robotics and Automation, pp. 1628–1633 (2008)Google Scholar
  12. 12.
    Kim, J., Iwamoto, K., Kuffner, J.J., Ota, Y., Pollard, N.S.: Physically based grasp quality evaluation under pose uncertainty. IEEE Trans. Robot. 29(6), 1424–1439 (2013)CrossRefGoogle Scholar
  13. 13.
    Kry, P.G., Pai, D.K.: Interaction capture and synthesis. ACM Trans. Graph. (SIGGRAPH) 25(3), 872–880 (2006)CrossRefGoogle Scholar
  14. 14.
    Kyota, F., Saito, S.: Fast grasp synthesis for various shaped objects. Comput. Graph. Forum (Eurographics) 31(2), 765–774 (2012)CrossRefGoogle Scholar
  15. 15.
    Lau, M., Dev, K., Shi, W., Dorsey, J., Rushmeier, H.: Tactile mesh saliency. ACM Trans. Graph. (SIGGRAPH) 35(4), 52:1–52:11 (2016)CrossRefGoogle Scholar
  16. 16.
    Li, Y., Fu, J.L., Pollard, N.S.: Data-driven grasp synthesis using shape matching and task-based pruning. IEEE Trans. Vis. Comput. Graph. 13(4), 732–747 (2007)CrossRefGoogle Scholar
  17. 17.
    Li, Y., Saut, J.P., Pettré, J., Sahbani, A., Bidaud, P., Multon, F.: Fast grasp planning by using cord geometry to find grasping points. In: IEEE International Conference on Robotics and Automation, pp. 3265–3270 (2013)Google Scholar
  18. 18.
    Miller, A.T., Allen, P.K.: GraspIt! A versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004)CrossRefGoogle Scholar
  19. 19.
    Miller, A.T., Knoop, S., Christensen, H.I., Allen, P.K.: Automatic grasp planning using shape primitives. In: IEEE International Conference on Robotics and Automation, pp. 1824–1829 (2003)Google Scholar
  20. 20.
    Nooruddin, F.S., Turk, G.: Simplification and repair of polygonal models using volumetric techniques. IEEE Trans. Vis. Comput. Graph. 9(2), 191–205 (2003)CrossRefGoogle Scholar
  21. 21.
    Park, Y.C., Starr, G.P.: Grasp synthesis of polygonal objects using a three-fingered robot hand. Int. J. Robot. Res. 11(3), 163–184 (1992)CrossRefGoogle Scholar
  22. 22.
    Pollard, N.S., Zordan, V.B.: Physically based grasping control from example. In: ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 311–318 (2005)Google Scholar
  23. 23.
    Przybylski, M., Asfour, T., Dillmann, R.: Unions of balls for shape approximation in robot grasping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1592–1599 (2010)Google Scholar
  24. 24.
    Rimon, E., Burdick, J.: On force and form closure for multiple finger grasps. In: IEEE International Conference on Robotics and Automation, pp. 1795–1800 (1996)Google Scholar
  25. 25.
    Roa, M.A., Suárez, R.: Grasp quality measures: review and performance. Auton. Robots 38(1), 65–88 (2015)CrossRefGoogle Scholar
  26. 26.
    Rosales, C., Ros, L., Porta, J.M., Suárez, R.: Synthesizing grasp configurations with specified contact regions. Int. J. Robot. Res. 30(4), 431–443 (2011)CrossRefGoogle Scholar
  27. 27.
    Sahbani, A., El-Khoury, S., Bidaud, P.: An overview of 3D object grasp synthesis algorithms. Robot. Auton. Syst. 60(3), 326–336 (2012)CrossRefGoogle Scholar
  28. 28.
    Shen, C.H., Fu, H., Chen, K., Hu, S.M.: Structure recovery by part assembly. ACM Trans. Graph. (SIGGRAPH Asia) 31(6), 180:1–180:12 (2012)Google Scholar
  29. 29.
    Ye, Y., Liu, C.K.: Synthesis of detailed hand manipulations using contact sampling. ACM Trans. Graph. (SIGGRAPH) 31(4), 41:1–41:10 (2012)CrossRefGoogle Scholar
  30. 30.
    Zhao, W., Zhang, J., Min, J., Chai, J.: Robust realtime physics-based motion control for human grasping. ACM Trans. Graph. (SIGGRAPH Asia) 32(6), 207:1–207:12 (2013)Google Scholar
  31. 31.
    Zhou, Q., Panetta, J., Zorin, D.: Worst-case structural analysis. ACM Trans. Graph. (SIGGRAPH) 32(4), 137:1–137:12 (2013)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaHefeiChina

Personalised recommendations