Skip to main content
SpringerLink
Log in

Grasp planning via hand-object geometric fitting

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. Regularity indicates if a polygon/polyhedron has all the same qualitative angles and all the edges of similar length (regular), or not (irregular). We normalize the regularity value within range (0,1].

  2. In the signed distance transform map, empty, partial, and full voxels are assigned positive, zero, and negative distance values, respectively.

References

  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)

    Article  Google Scholar 

  2. Attene, M., Falcidieno, B., Spagnuolo, M.: Hierarchical mesh segmentation based on fitting primitives. Vis. Comput. 22(3), 181–193 (2006)

    Article  Google Scholar 

  3. Aydin, Y., Nakajima, M.: Database guided computer animation of human grasping using forward and inverse kinematics. Comput. Graph. 23(1), 145–154 (1999)

    Article  Google Scholar 

  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)

  5. Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis—a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)

    Article  Google Scholar 

  6. Chalmeta, R., Hurtado, F., Sacristán, V., Saumell, M.: Measuring regularity of convex polygons. Comput. Aided Des. 45(2), 93–104 (2013)

    Article  MathSciNet  Google Scholar 

  7. Ciocarlie, M.T., Allen, P.K.: Hand posture subspaces for dexterous robotic grasping. Int. J. Robot. Res. 28(7), 851–867 (2009)

    Article  Google Scholar 

  8. Ding, L., Ding, X., Fang, C.: 3D face sparse reconstruction based on local linear fitting. Vis. Comput. 30(2), 189–200 (2014)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  13. Kry, P.G., Pai, D.K.: Interaction capture and synthesis. ACM Trans. Graph. (SIGGRAPH) 25(3), 872–880 (2006)

    Article  Google Scholar 

  14. Kyota, F., Saito, S.: Fast grasp synthesis for various shaped objects. Comput. Graph. Forum (Eurographics) 31(2), 765–774 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  18. Miller, A.T., Allen, P.K.: GraspIt! A versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  25. Roa, M.A., Suárez, R.: Grasp quality measures: review and performance. Auton. Robots 38(1), 65–88 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  27. Sahbani, A., El-Khoury, S., Bidaud, P.: An overview of 3D object grasp synthesis algorithms. Robot. Auton. Syst. 60(3), 326–336 (2012)

    Article  Google Scholar 

  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. Ye, Y., Liu, C.K.: Synthesis of detailed hand manipulations using contact sampling. ACM Trans. Graph. (SIGGRAPH) 31(4), 41:1–41:10 (2012)

    Article  Google Scholar 

  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. Zhou, Q., Panetta, J., Zorin, D.: Worst-case structural analysis. ACM Trans. Graph. (SIGGRAPH) 32(4), 137:1–137:12 (2013)

    MATH  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Peng Song, Zhongqi Fu & Ligang Liu

Authors
  1. Peng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongqi Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ligang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Song.

Electronic supplementary material

Below is the link to the electronic supplementary material.

371_2016_1333_MOESM1_ESM

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, P., Fu, Z. & Liu, L. Grasp planning via hand-object geometric fitting. Vis Comput 34, 257–270 (2018). https://doi.org/10.1007/s00371-016-1333-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-016-1333-x

Keywords

Search

Navigation