Folding clothes autonomously: A complete pipeline
Abstract
This work presents a complete pipeline for folding a pile of clothes using a dual-armed robot. This is a challenging task both from the viewpoint of machine vision and robotic manipulation. The presented pipeline is comprised of the following parts: isolating and picking up a single garment from a pile of crumpled garments, recognizing its category, unfolding the garment using a series of manipulations performed in the air, placing the garment roughly flat on a work table, spreading it, and, finally, folding it in several steps. The pile is segmented into separate garments using color and texture information, and the ideal grasping point is selected based on the features computed from a depth map. The recognition and unfolding of the hanging garment are performed in an active manner, utilizing the framework of active random forests to detect grasp points, while optimizing the robot actions. The spreading procedure is based on the detection of deformations of the garment's contour. The perception for folding employs fitting of polygonal models to the contour of the observed garment, both spread and already partially folded. We have conducted several experiments on the full pipeline producing very promising results. To our knowledge, this is the first work addressing the complete unfolding and folding pipeline on a variety of garments, including T-shirts, towels, and shorts.
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For removing the final wrinkles on an article, spreading is a more effective action primitive than P&P.
“For removing the final wrinkles on the article, the system can still use P&P [256], but spreading is a more effective action primitive [253], where one arm fixes the cloth from sliding and the other sweeps to an effective direction.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [253]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Medical Entomology and Zoology · Cited by 7,121 · see citing works
Digital Image Processing Using MATLAB
González, R. C., Woods, R. E., & Eddins, S. L. (2003) · Cited by 7,014 · see citing works
Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,205 · see citing works
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703 · see citing works
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0347-9_3 · Cited by 5,053 · see citing works
RRT-connect: An efficient approach to single-query path planning
Kuffner, J. J., & LaValle, S. M. (2000) · Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065) · doi:10.1109/robot.2000.844730 · Cited by 3,555 · see citing works
The Open Motion Planning Library
Sucan, I. A., Moll, M., & Kavraki, L. E. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2205651 · Cited by 1,616 · see citing works
Shape Classification Using the Inner-Distance
Ling, H., & Jacobs, D. W. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.41 · Cited by 1,069 · see citing works
Criminisi, A., Shotton, J., & Konukoglu, E. (2012) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000035 · Cited by 677 · see citing works
Orchard, M. T., & Bouman, C. A. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.107417 · Cited by 489 · see citing works
Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., & Abbeel, P. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509439 · Cited by 418 · see citing works
Efficient regression of general-activity human poses from depth images
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., & Fitzgibbon, A. W. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126270 · Cited by 347 · see citing works
Multiscale detection of curvilinear structures in 2-D and 3-D image data
Koller, T., Gerig, G., Szekely, G., et al. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466846 · Cited by 314 · see citing works
Information theoretic sensor data selection for active object recognition and state estimation
Denzler, J., & Brown, C. M. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982896 · Cited by 257 · see citing works
A geometric approach to robotic laundry folding
Miller, S., van den Berg, J., Fritz, M., Darrell, T., Goldberg, K., & Abbeel, P. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911430417 · Cited by 238 · see citing works
Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests
Tang, D., Yu, T.-H., & Kim, T. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.400 · Cited by 220 · see citing works
Self-citationOptimum polygonal approximation of digitized curves
Pérez, J.-C., & Vidal, E. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90002-7 · Cited by 216 · see citing works
Criminisi, A., Ender Konukoglu, A., & Shotton, J. (2011) · Cited by 204 · see citing works
Home-Assistant Robot for an Aging Society
Yamazaki, K., Ueda, R., Nozawa, S., et al. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2200563 · Cited by 186 · see citing works
Bringing clothing into desired configurations with limited perception
Cusumano-Towner, M., Singh, A., Miller, S., O’Brien, J. F., & Abbeel, P. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980327 · Cited by 144 · see citing works
Doumanoglou, A., Kargakos, A., Kim, T.-K., & Malassiotis, S. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906974 · Cited by 123 · see citing works
Self-citationUsing depth and appearance features for informed robot grasping of highly wrinkled clothes
Arnau Ramisa, A., Alenyà, G., Moreno-Noguer, F., & Carme Torras, A. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225045 · Cited by 122 · see citing works
Folding deformable objects using predictive simulation and trajectory optimization
Li, Y., Yue, Y., Xu, D., Grinspun, E., & Allen, P. K. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354231 · Cited by 103 · see citing works
Bimanual robotic cloth manipulation for laundry folding
Bersch, C., Pitzer, B., & Kammel, S. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6095109 · Cited by 98 · see citing works
Sun, L., Aragón-Camarasa, G., Rogers, S., & Siebert, J. P. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7138998 · Cited by 94 · see citing works
Parametrized shape models for clothing
Miller, S. D., Fritz, M., Darrell, T., & Abbeel, P. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980453 · Cited by 92 · see citing works
Classification of clothing using interactive perception
Willimon, B., Birchfield, S. T., & Walker, I. D. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980336 · Cited by 87 · see citing works
Gravity-Based Robotic Cloth Folding
van den Berg, J., Miller, S., Goldberg, K., & Abbeel, P. (2010) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-17452-0_24 · Cited by 75 · see citing works
Garment perception and its folding using a dual-arm robot
Stria, J., Průša, D., Hlaváč, V., Wagner, L., Petrík, V., Kršek, P., & Smutný, V. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942541 · Cited by 75 · see citing works
Self-citationClothes state recognition using 3D observed data
Kita, Y., Ueshiba, T., Neo, E., & Kita, N. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152741 · Cited by 73 · see citing works
Recognition of deformable object category and pose
Li, Y., Chen, C.-F., & Allen, P. K. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907676 · Cited by 67 · see citing works
Real-time pose estimation of deformable objects using a volumetric approach
Li, Y., Wang, Y., Case, M., Chang, S., & Allen, P. K. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942687 · Cited by 66 · see citing works
Regrasping and unfolding of garments using predictive thin shell modeling
Li, Y., Xu, D., Yue, Y., Wang, Y., Chang, S., Grinspun, E., & Allen, P. K. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139370 · Cited by 62 · see citing works
Model for unfolding laundry using interactive perception
Willimon, B., Birchfield, S. T., & Walker, I. D. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6095066 · Cited by 60 · see citing works
Model for unfolding laundry using interactive perception
Willimon, B., Birchfield, S. T., & Walker, I. D. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6048796 · Cited by 50 · see citing works
Automatic Visual Recognition of Deformable Objects for Grasping and Manipulation
Foresti, G. L., & Pellegrino, F. A. (2004) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2003.819701 · Cited by 47 · see citing works
Active Random Forests: An Application to Autonomous Unfolding of Clothes
Doumanoglou, A., Kim, T., Zhao, X., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_42 · Cited by 45 · see citing works
Self-citationPlanning strategy for task of unfolding clothes
Hamajima, K., & Kakikura, M. (2000) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(99)00115-3 · Cited by 45 · see citing works
Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition
Laporte, C., & Arbel, T. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-005-4436-9 · Cited by 45 · see citing works
Head pose estimation: Classification or regression?
Guo, G., Fu, Y., Dyer, C. R., et al. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761081 · Cited by 45 · see citing works
On the Development of a Specialized Flexible Gripper for Garment Handling
Le, T.-H.-L., Jilich, M., Landini, A., Zoppi, M., Zlatanov, D., & Molfino, R. (2013) · Journal of Automation and Control Engineering · doi:10.12720/joace.1.3.255-259 · Cited by 45 · see citing works
A deformable model driven visual method for handling clothes
Kita, Y., Saito, F., & Kita, N. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1308874 · Cited by 43 · see citing works
A model-driven method of estimating the state of clothes for manipulating it
Kita, Y., & Kita, N. (2003) · Proceedings · doi:10.1109/acv.2002.1182158 · Cited by 35 · see citing works
FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation
Arnau Ramisa, A., Alenyà, G., Moreno-Noguer, F., & Carme Torras, A. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696446 · Cited by 32 · see citing works
Robot system for cloth handling
Hata, S., Hiroyasu, T., Hayashi, J., et al. (2008) · doi:10.1109/iecon.2008.4758515 · Cited by 23 · see citing works
Planning strategy for task untangling laundry-isolating clothes from a washed mass
Hamajima, K., Tokyo Denki University, 2.-2. N.-C., & Kakikura, M. (1998) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.1998.p0244 · Cited by 22 · cited 2× · see citing works
“Van den Berg et al. [28] introduced algorithms for folding a single garment spread on a table.”
BackgroundMethodStria, J., Průša, D., & Hlaváč, V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10401-0_16 · Cited by 22 · see citing works
Self-citationClothes handling using visual recognition in cooperation with actions
Kita, Y., Neo, E., Ueshiba, T., & Kita, N. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5651222 · Cited by 21 · see citing works
3D non-rigid deformable surface estimation without feature correspondence
Willimon, B., Walker, I. D., & Birchfield, S. T. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630641 · Cited by 18 · see citing works
An energy minimization approach to 3D non-rigid deformable surface estimation using RGBD data
Willimon, B., Hickson, S., Walker, I. D., & Birchfield, S. T. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6386213 · Cited by 16 · see citing works
Characterization of textile grasping experiments
Alenyà, G., Arnau Ramisa, A., Moreno-Noguer, F., & Carme Torras, A. (2012) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 7 · see citing works
The Calibration of the Pan-Tilt Units for the Active Stereo Head
Schmidt, A., Sun, L., Aragón-Camarasa, G., et al. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-23814-2_25 · Cited by 3 · see citing works
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Modeling, learning, perception, and control methods for deformable object manipulation
Yin, H., Varava, A., & Kragic, D. (2021) · Science Robotics · doi:10.1126/scirobotics.abd8803 · Cited by 299 · see citing works
A framework for robotic clothing assistance by imitation learning
Joshi, R. P., Koganti, N., & Shibata, T. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1636715 · Cited by 50 · see citing works
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy
Weng, T., Bajracharya, S., Wang, Y., Agrawal, K., & Held, D. (2021) · arXiv · doi:10.48550/arxiv.2111.05623 · Cited by 48 · see citing works
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“For removing the final wrinkles on the article, the system can still use P&P [256], but spreading is a more effective action primitive [253], where one arm fixes the cloth from sliding and the other sweeps to an effective direction.”
BackgroundMethodGoal-Conditioned Model Simplification for 1-D and 2-D Deformable Object Manipulation
Wang, S., Leonetti, M., & Dogar, M. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3577052 · Cited by 4 · see citing works
“For example, controlling the shape of a rope by manipulating only its two ends [16], folding or unfolding cloth by focusing on its visible corners [17], [18], [19], or removing wrinkles from fabric [20].”
- Authors
- Doumanoglou, A., Stria, J., Peleka, G., Mariolis, I., Petrik, V., Kargakos, A., Wagner, L., Hlavac, V., Kim, T.-K., & Malassiotis, S.
- Affiliations
- Centre for Research and Technology Hellas (GR)
- Czech Technical University in Prague (CZ)
- Imperial College London (GB)
- Venue
- IEEE Transactions on Robotics
- Published
- 2016-12
- Volume
- 32
- Issue
- 6
- Pages
- 1461-1478
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2530616813
- Cited by
- 122
- References
- 56
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 18 · 2023: 19 · 2022: 21 · 2021: 10 · 2020: 14 · 2019: 7 · 2018: 8 · 2017: 5
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- Funders
- European Commission (FP7-ICT-288553)
- CloPeMa (Clothes Perception and Manipulation)
- TRADR (FP7-ICT-609763)
- Technology Agency of the Czech Republic (TE01020197)
- Agency of the Czech Technical University in Prague (SGS15/204/OHK3/3T/13, SGS15/203/OHK3/3T/13)
- Engineering and Physical Sciences Research Council (EP/J012106/1)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- dual-arm robot, robotic manipulation, depth images
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