Skip to main content
SpringerLink
Log in

VReason Grasp: An Ordered Grasp Based on Physical Intuition in Stacking Objects

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

  • 1811 Accesses

Abstract

The supporting semantics plays a vital role in the robotic grasping task. However, it is difficult to identify the spatial relationship of the objects and grasp them orderly without falling. This paper presents a Visual Reasoning Grasp Framework (VRGF) to address the problems mentioned above. The VRGF consists of two parts: 3D semantic segmentation and Supporting Relationship Reasoning (SRR). We adopt the fused projection for 3D semantic segmentation, which can reduce the processing time and promote the performance of the reasoning framework. The SRR can distinguish how objects support each other in the stacking scenarios. We also design a complete robotic system to verify the robustness and generalization of the framework in the real world. The experiment results demonstrate the reliability and robustness of the VRGF’s grasping strategy for stacking objects.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dai, A., Nießner, M.: 3DMV: joint 3D-multi-view prediction for 3D semantic scene segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 452–468 (2018)

    Google Scholar 

  2. Feng, Q., Chen, Z., Deng, J., Gao, C., Zhang, J., Knoll, A.: Center-of-mass-based robust grasp planning for unknown objects using tactile-visual sensors. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 610–617. IEEE (2020)

    Google Scholar 

  3. Hodson, R.: How robots are grasping the art of gripping. Nature 557(7706), S23–S23 (2018)

    Article  Google Scholar 

  4. Huynh, J.: Separating axis theorem for oriented bounding boxes (2009). https://jkh.me/files/tutorials/Separating%20Axis%20Theorem%20for%20Oriented%20Bounding%20Boxes.pdf

  5. Jia, Z., Gallagher, A.C., Saxena, A., Chen, T.: 3D reasoning from blocks to stability. IEEE Trans. Pattern Anal. Mach. Intell. 37(5), 905–918 (2014)

    Article  Google Scholar 

  6. Marton, Z.C., Goron, L., Rusu, R.B., Beetz, M.: Reconstruction and verification of 3D object models for grasping. In: Robotics Research, pp. 315–328. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-19457-3_19

  7. Mojtahedzadeh, R., Bouguerra, A., Schaffernicht, E., Lilienthal, A.J.: Support relation analysis and decision making for safe robotic manipulation tasks. Robot. Auton. Syst. 71, 99–117 (2015)

    Article  Google Scholar 

  8. Panda, S., Hafez, A.A., Jawahar, C.: Learning support order for manipulation in clutter. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 809–815. IEEE (2013)

    Google Scholar 

  9. ten Pas, A., Gualtieri, M., Saenko, K., Platt, R.: Grasp pose detection in point clouds. Int. J. Robot. Res. 36(13–14), 1455–1473 (2017)

    Google Scholar 

  10. Peng, W., Ao, Y., He, J., Wang, P.: Vehicle odometry with camera-lidar-IMU information fusion and factor-graph optimization. J. Intell. Robot. Syst. 101(4), 1–13 (2021). https://doi.org/10.1007/s10846-021-01329-x

    Article  Google Scholar 

  11. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413 (2017)

  12. Qian, J., Weng, T., Okorn, B., Zhang, L.: Cloth region segmentation for robust grasp selection. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9553–9560 (2020)

    Google Scholar 

  13. Vo, A.V., Truong-Hong, L., Laefer, D.F., Bertolotto, M.: Octree-based region growing for point cloud segmentation. ISPRS J. Photogramm. Remote. Sens. 104, 88–100 (2015)

    Article  Google Scholar 

  14. Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: SqueezeSegV2: improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 4376–4382. IEEE (2019)

    Google Scholar 

  15. Xu, C., et al.: SqueezeSegV3: spatially-adaptive convolution for efficient point-cloud segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 1–19. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58604-1_1

    Chapter  Google Scholar 

  16. Zhang, H., Lan, X., Wan, L., Yang, C., Zhou, X., Zheng, N.: RPRG: toward real-time robotic perception reasoning and grasping with one multi-task convolutional neural network, pp. 1–7. arXiv preprint arXiv:1809.07081 (2018)

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Fudan University, Shanghai, China

    Xiang Ji, Qiushu Chen & Tao Xiong

  2. Academy for Engineering and Technology, Fudan University, Shanghai, China

    Tianyu Xiong

  3. Department of Electronic Engineering, School of Information Science and Technology, Research Center of Smart Networks and Systems, Fudan University, Shanghai, China

    Huiliang Shang

Authors
  1. Xiang Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiushu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianyu Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huiliang Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huiliang Shang .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ji, X., Chen, Q., Xiong, T., Xiong, T., Shang, H. (2022). VReason Grasp: An Ordered Grasp Based on Physical Intuition in Stacking Objects. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_70

Download citation

Publish with us

Policies and ethics

Search

Navigation