Type and Fabric Agnostic Methods for Robotic Unfolding of Folded Garments | Journal of Intelligent & Robotic Systems Skip to main content
Springer Nature Link
Log in

Type and Fabric Agnostic Methods for Robotic Unfolding of Folded Garments

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper introduces two type-agnostic and fabric-agnostic methods for robotic unfolding of garments. In the first method, a new path extraction procedure for unfolding the upper layer of a garment’s fold, which is located on a working table, with one robotic arm is proposed. The path is extracted using learning by demonstration methods and it is customized on each fold by integrating features extracted by a depth sensor, such as the fold’s curve height. Moreover, the unfolding path can be re-adjusted through the classification of visual data by Support Vector Machines. In the second introduced method, the garment is lifted in the air by two points so that it unfolds due to gravity while the criterion proposed for the selection of these grasp points is the maximization of the free of deformations hanging’s garment area. The proposed methods are evaluated experimentally in simulation and real environment while comparisons with similar approaches prove their advantages.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and materials

For any supplementary information on the paper’s data contact the corresponding author.

Code Availability

For any request contact the corresponding author.

References

  1. Sanchez, J., Corrales, J.A., Bouzgarrou, B.C., Mezouar, Y.: Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey. The International Journal of Robotics Research 37(7), 688–716 (2018)

    Article  Google Scholar 

  2. Foldimate: https://foldimate.com/, last access February (2022)

  3. Effie: https://helloeffie.com/, last access February (2022)

  4. Triantafyllou, D., Mariolis, I., Kargakos, A., Malassiotis, S., Aspragathos, N.: A geometric approach to robotic unfolding of garments. Journal of Robotics and Autonomous Systems 75, 233–243 (2016)

    Article  Google Scholar 

  5. Triantafyllou, D., Koustoumpardis, P., Aspragathos, N.: Type independent hierarchical analysis for the recognition of folded garments’ configuration. Inteligent Service Robotics 14, 427–444 (2021)

    Article  Google Scholar 

  6. Cusumano-Towner, M., Singh, A., Miller, S., O’Brien, J. F., Abbeel, P.: Bringing clothing into desired configurations with limited perception, Robotics and Automation (2011)

  7. Jimenez, P.: Grasp point localization, classification and state recognition in robotic manipulation of cloth: an overview. Robot. Auton. Syst. 92, 107–125 (2017)

    Article  Google Scholar 

  8. Doumanoglou, A., Kim, T.K., Zhao, X., Malassiotis, S.: Active Random forests: An application to Autonomous Clothes Unfolding, Computer Vision – ECCV (2014)

  9. Doumanoglou, A., Kargakos, A., Kim, T.K., Malassiotis, S.: Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning, Robotics and Automation (2014)

  10. Corona, E., Alenya, G., Gabas, A., Torras, C.: Active recognition and target grasping point detection using deep learning. Pattern Recogn. 74, 629–641 (2018)

    Article  Google Scholar 

  11. Mariolis, I., Peleka, G., Kargakos, A., Malassiotis, S.: Pose and category recognition of highly deformable objects using deep learning, Advanced Robotics (2015)

  12. Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., Abbeel, P.: Cloth Grasp Point Detection based on Multiple-View Geometric Cues with Application to Robotic Towel Folding, Robotics and Automation (2010)

  13. Yamazaki, K.: A method of grasp point selection from an item of clothing using hem element relations, Advanced Robotics (2015)

  14. Kita, Y., Sian Neo, E., Ueshiba, T., Kita, N.: Clothes handling using visual recognition in cooperation with actions, Intelligent Robots and Systems (2010)

  15. Li, Y., Wang, Y., Case, M., Chang, S.F., Allen, K.P.: Real-time estimation of deformable objects using a volumetric approach, IROS (2014)

  16. Manabu, K., Massayoshi, K.: Study on handling clothes-task planning of deformation for unfolding laundry. J. Robot. Mechatron. 15, 429–434 (2003)

    Google Scholar 

  17. Stria, J., Petrik, V., Hlavac, V.: Model free approach to garments unfolding based on detection of folded layers, Intelligent Robots and Systems (IROS) (2017)

  18. Estevez, D., Victores, J.D., Fernandez, R., et al: Enabling garment-agnostic laundry tasks for a Robot Household Companion. Robot. Auton. Syst. 123, 134–139 (2020)

    Article  Google Scholar 

  19. Triantafyllou, D., Aspragathos, N.: Upper layer extraction of a folded garment towards unfolding by a robot. International Conference on Robotics in Alpe-Adria Danube Region 67, 597–604 (2019)

    Google Scholar 

  20. Estevez, D., Victores, J., Balaguer, C.: Future Trends in Perception and Manipulation for Unfolding and Folding Garments (2016)

  21. Yuba, H., Yamazaki, K.: Unfolding an item of rectangular clothing using a single arm and an assistant instrument, System Integration (2014)

  22. Willimon, B., Birchfield, S., Walker, I.: Model for unfolding laundry using interactive perception, Intelligent Robots and Systems (2011)

  23. Van den Berg, J., Miller, S.D., Goldberg, K.Y., Abbeel, P.: Gravity-Based Robotic cloth folding. Springer Tracts Adv. Robot. 68, 409–424 (2010)

    Article  Google Scholar 

  24. Pau, F.W.: Acquisition, placement, and folding of fabric materials. Cloth. Sci. Technol 16(1), 227–237 (2004)

    Google Scholar 

  25. Petrík, V., Smutný, V., Krsek, P., Hlaváč, V.: Single arm robotic garment folding path generation. Adv. Robot. 31(23–24), 1325–1337 (2017)

    Article  Google Scholar 

  26. Li, Y., Yue, Y., Xu, D., Grinspun, E., Allen, P. K.: Folding deformable objects using predictive simulation and path optimization, Intelligent Robots and Systems (2015)

  27. Zoumponos, G., Aspragathos, A.: A fuzzy strategy for the robotic folding of fabrics with machine vision feedback. Ind. Robot. 37(3), 302–308 (2010)

    Article  Google Scholar 

  28. Petrík, V., Smutny, V., Krsek, P., Hlavac, V.: Robotic garment folding: precision improvement and workspace enlargement. Autonomous Robotic Systems (TAROS) 9287, 204–225 (2015)

    Article  Google Scholar 

  29. Berndt, D.J., Clifford, J.: Using dynamic time warping to findpatterns in time series. Knowledge Discovery in Databases Workshop 10, 359–370 (1994)

    Google Scholar 

  30. Cohn, D., Ghahramani, Z., Jordan, M.: Active learning with statisticalmodels. J. Artif. Intell. Res. 4, 129–145 (1996)

    Article  Google Scholar 

  31. Calinon, S., Guenter, F., Billard, A: On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(2), 286–298 (2007)

    Article  Google Scholar 

  32. Nathan, D., Bruls, O., Jacques, T.: Programming by demonstration using fiducial markers, 3rd Robotix-Academy Conference on for Industrials Robotics (2019)

  33. Müller, M.: Dynamic Time Warping, Music and motion, pp 69–84. Springer, Heidelberg (2007)

    Google Scholar 

  34. Mohssen, M., Bad, M., Bashier, E.: Gaussian Mixture Model, Machine Learning, OREILLY (2015)

  35. Geron, A.: Hands on Machine Learning with Scikit-Learn, Keras and Tensorflow, O Reilly (2019)

  36. Najman, L., Schmitt, M.: Watershed of a continuous function. Signal Processing (Special issue on Mathematical Morphology.) 38, 99–112 (1994)

    Google Scholar 

  37. Hershberger, S.J.: An o (nlogn) implementation of the Douglas–Peucker algorithm for line simplification. In: Proceedings of the 10th annual symposium on computational geometry, pp 383–384 (1994)

  38. Blender Online Community: Blender - a 3D modelling and rendering package, Blender foundation, Stichting Blender Foundation, Amsterdam, http://www.blender.org (2018)

  39. ORBBEC: https://orbbec3d.com/, last access February (2022)

  40. Niryo: https://niryo.com/product/niryo-one/, last access February (2022)

  41. Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for path clustering in outdoor surveillance scenes, pattern recognition (ICPR’06) hong kong (2006)

Download references

Funding

No Funding was received.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26504, Greece

    Dimitra Triantafyllou, Panagiotis Koustoumpardis & Nikolaos Aspragathos

Authors
  1. Dimitra Triantafyllou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Panagiotis Koustoumpardis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikolaos Aspragathos
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dimitra Triantafyllou: research, implementation, text composition, Panagiotis Koustoumpardis: discussion, Nikolaos Aspragathos: discussion and supervision

Corresponding author

Correspondence to Dimitra Triantafyllou.

Ethics declarations

Ethics approval

All authors confirm that there is no potential acts of misconduct.

Consent for Publication

The authors consent to publish.

Conflict of interest/Competing interests

No known conflicts of interest.

Consent to participate

The authors consent to participate.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Triantafyllou, D., Koustoumpardis, P. & Aspragathos, N. Type and Fabric Agnostic Methods for Robotic Unfolding of Folded Garments. J Intell Robot Syst 105, 62 (2022). https://doi.org/10.1007/s10846-022-01641-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01641-0

Keywords

Search

Navigation