A Heuristic-Based Approach for Flattening Wrinkled Clothes

Robotics and Autonomous Systems

Siebert

et al. 2019

Self Cite

We present a robot vision approach to deformable object classification, with direct application to autonomous service robots. Our approach is based on the assumption that continuous perception provides robots with greater visual competence for deformable objects interpretation and classification. Our approach classifies the category of clothing items by continuously perceiving the dynamic interactions of the garment's material and shape as it is being picked up. For this, we extract continuously visual features of a RGB-D video sequence and fusing features by means of the Locality Constrained Group Sparse Representation (LGSR) algorithm. To evaluate the performance of our approach, we created a fully annotated database featuring 150 garment videos in random configurations. Experiments demonstrate that by continuously observing an object deform, our approach achieves a classification score of 66.7%, outperforming state-of-the-art approaches by a ⇠ 27.3% increase.

Section: Related Workmentioning

confidence: 99%

“…3. We describe a database of di↵erent clothing items as they are being picked up by a robot, which we use to validate our continuous perception approach alongside with state-of-the-art clothing databases [6].…”

Section: Introductionmentioning

confidence: 99%

Continuous perception for deformable objects understanding

Ruiz‐del‐Solar

Robotics and Autonomous Systems

Siebert

et al. 2019

Self Cite

“…Moreover, interactive perception has been used in heuristic-based generic clothing manipulation. In [8] and our previous work [12], [21], a perception-manipulation cycle is adapted to track the state of the garment and heuristic manipulation strategies are used to unfold and flatten the garment on the table.…”

Section: Related Workmentioning

confidence: 99%

Recognising the clothing categories from free-configuration using Gaussian-Process-based interactive perception

2016 IEEE International Conference on Robotics and Automation (ICRA)

Rogers

Aragon-Camarasa

et al. 2016

Self Cite

Abstract-In this paper, we propose a Gaussian Processbased interactive perception approach for recognising highlywrinkled clothes. We have integrated this recognition method within a clothes sorting pipeline for the pre-washing stage of an autonomous laundering process. Our approach differs from reported clothing manipulation approaches by allowing the robot to update its perception confidence via numerous interactions with the garments. The classifiers predominantly reported in clothing perception (e.g. SVM, Random Forest) studies do not provide true classification probabilities, due to their inherent structure. In contrast, probabilistic classifiers (of which the Gaussian Process is a popular example) are able to provide predictive probabilities. In our approach, we employ a multi-class Gaussian Process classification using the Laplace approximation for posterior inference and optimising hyper-parameters via marginal likelihood maximisation. Our experimental results show that our approach is able to recognize unknown garments in difficult configurations using limited visual perception and demonstrates a substantial improvement over non-interactive perception approaches.

“…A force is applied to the intersection point of the perpendicular bisector and the cloth border (the closer border with respect to the wrinkle's centre) along the direction of the perpendicular bisector in order to flatten the cloth. It should be noted that, instead of applying a force of fixed magnitude, as in [6], the magnitude of the force is now related to the wrinkle's score in this implementation. A simulated flattening video demo is available at: https:// youtu.be/Rd7hCUZaTx0.…”

Section: Heuristic-based Flattening Approachmentioning

confidence: 99%

A Precise Method for Cloth Configuration Parsing Applied to Single-Arm Flattening

International Journal of Advanced Robotic Systems

Aragon-Camarasa

Khan

et al. 2016

Self Cite

In this paper, we investigate the contribution that visual perception affords to a robotic manipulation task in which a crumpled garment is flattened by eliminating visually detected wrinkles. In order to explore and validate visually guided clothing manipulation in a repeatable and controlled environment, we have developed a hand-eye interactive virtual robot manipulation system that incorporates a clothing simulator to close the effector-garment-visual sensing interaction loop. We present the technical details and compare the performance of two different methods for detecting, representing and interpreting wrinkles within clothing surfaces captured in high-resolution depth maps. The first method we present relies upon a clustering-based method for localizing and parametrizing wrinkles, while the second method adopts a more advanced geometrybased approach in which shape-topology analysis underpins the identification of the cloth configuration (i.e., maps wrinkles). Having interpreted the state of the cloth configuration by means of either of these methods, a heuristicbased flattening strategy is then executed to infer the appropriate forces, their directions and gripper contact locations that must be applied to the cloth in order to flatten the perceived wrinkles. A greedy approach, which attempts to flatten the largest detected wrinkle for each perception-iteration cycle, has been successfully adopted in this work. We present the results of our heuristic-based flattening methodology which relies upon clustering-based and geometry-based features respectively. Our experiments indicate that geometry-based features have the potential to provide a greater degree of clothing configuration understanding and, as a consequence, improve flattening performance. The results of experiments using a real robot (as opposed to simulated robot) also confirm our proposition that a more effective visual perception system can advance the performance of cloth manipulation.