This document presents a garment-agnostic approach for robotic garment unfolding. The proposed approach uses a 3D mesh reconstructed from an RGB-D sensor to segment and cluster the garment. Regions are selected for unfolding based on bumpiness. Experiments using an industrial robot on 6 garment categories showed a 20% performance improvement over the previous approach. Future work includes improving segmentation of thin garments and experimenting with tactile feedback during manipulation.

1. Improving and Evaluating
Robotic Garment Unfolding: A
Garment-Agnostic Approach
David Estevez*, Raul Fernandez-Fernandez,
Juan G. Victores, Carlos Balaguer
Robotics Lab research Group
Universidad Carlos III de Madrid
{destevez, rauferna, jcgvicto, balaguer}@ing.uc3m.es

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About me
David Estevez
Ph.D candidate at Carlos III University of
Madrid (Spain)
Thesis topic: robotic systems for the
perception and manipulation of deformable
objects (such a garments)
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Structure
Introduction to the problem
Proposed solution
Results and conclusions
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Introduction
Problem: Unfolding Garments
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Introduction
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Typical laundry pipeline
1.Pick a Garment 2.Unfold Garment 3.Iron Garment 4.Fold Garment

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Introduction
Human-based Large machines
Actual need in industrial environments
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Introduction
Actual need in domestic environments
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Elder people Lazy peopleDisabled people

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Introduction
CloPeMa (Clothes Perception and
Manipulation)
European Project (2012-2015)
(Doumanoglou et al., 2014)
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State of the Art
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Two approaches:
Modeling-Based Approaches
Manipulation-Based Approaches

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State of the Art
Modeling-Based Approaches
(Miller et al., 2011)
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State of the Art
Manipulation-Based Approaches
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(Cusumano-Towner et al., 2011)

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Architecture
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Algorithm
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Contributions (with respect to our previous work):
● Input data is now a 3D mesh reconstructed by Kinect
Fusion.
● Segmentation is performed with RANSAC (color
independent).
● Manipulation of the garment is performed with place point
symmetrical to fold edge.
● Experiments executed with industrial manipulator to obtain
quantitative results.

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Algorithm
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Assumptions:
● A single garment has been already selected from a pile of
(unordered) garments.
● The garment has been laid as flat as possible with a simple
manipulation operation (2 random grasping points).

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Architecture
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Garment scan
RGB-D Sensor

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Architecture
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Garment scan (II)

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Architecture
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Garment segmentation
Segmentation mask
generated by RANSAC
Garment simplified
contour

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Architecture
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Garment depth map clustering
Watershed algorithm
Height clusters

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Architecture
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Garment pick and place points
Selection based on Bumpiness:

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Experiments and Results
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●Experiments performed with an ABB IRB
240 industrial manipulator.
●6 garment categories, 5 trials per category (3
with 1 fold and 2 with 2 folds).
●Comparison between old approach and new
approach.

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Experiments and Results
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Experiments and Results
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Conclusions
Performance improvement with respect to old
approach (20%)
Very thin garments still yield bad results due
to sensor resolution
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Future work
Improve segmentation of thin garments
with color information.
Experiment with different clustering
algorithms.
Improve manipulation with tactile
feedback, orientation control.
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25. Improving and Evaluating
Robotic Garment Unfolding: A
Garment-Agnostic Approach
Thank you!
David Estevez*, Raul Fernandez-Fernandez,
Juan G. Victores, Carlos Balaguer