Improving and evaluating robotic garment unfolding: A garment-agnostic approach

Current approaches for robotic garment folding require a full view of an extended garment, in order to successfully apply a model-based folding sequence. In this paper, we present a garment-agnostic algorithm that requires no model to unfold clothes and works using only depth data. Once the garment is unfolded, state of the art approaches for folding may be applied. The algorithm presented is divided into 3 main stages. First, a Segmentation stage extracts the garment data from the background, and approximates its contour into a polygon. Then, a Clustering stage groups regions of similar height within the garment, corresponding to different overlapped regions. Finally, a Pick and Place Points stage finds the most suitable points for grasping and releasing the garment for the unfolding process, based on a bumpiness value defined as the accumulated difference in height along selected candidate paths. Experiments for evaluation of the vision algorithm have been performed over a dataset of 30 samples from a total of 6 different garment categories with one and two folds. The whole unfolding algorithm has also been validated through experiments with an industrial robot platform over a subset of the dataset garments.

[1]  Peter K. Allen,et al.  Recognition of deformable object category and pose , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Shih-Fu Chang,et al.  Regrasping and unfolding of garments using predictive thin shell modeling , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[3]  James F. O'Brien,et al.  Bringing clothing into desired configurations with limited perception , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[5]  Raul Fernandez-Fernandez,et al.  Robotic ironing with a humanoid robot using human tools , 2017, 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[6]  Trevor Darrell,et al.  Parametrized shape models for clothing , 2011, 2011 IEEE International Conference on Robotics and Automation.

[7]  Nikos A. Aspragathos,et al.  A geometric approach to robotic unfolding of garments , 2016, Robotics Auton. Syst..

[8]  Shih-Fu Chang,et al.  Real-time pose estimation of deformable objects using a volumetric approach , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Nobuyuki Kita,et al.  Clothes state recognition using 3D observed data , 2009, 2009 IEEE International Conference on Robotics and Automation.

[10]  Hiroaki Seki,et al.  Unfolding of Massive Laundry and Classification Types by Dual Manipulator , 2007, J. Adv. Comput. Intell. Intell. Informatics.

[11]  Ian D. Walker,et al.  A new approach to clothing classification using mid-level layers , 2013, 2013 IEEE International Conference on Robotics and Automation.

[12]  Carlos Balaguer,et al.  Towards Robotic Garment Folding: A Vision Approach for Fold Detection , 2016, 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[13]  Václav Hlavác,et al.  Polygonal Models for Clothing , 2014, TAROS.

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Tae-Kyun Kim,et al.  Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).