Irene Garcia-Camacho1, Alberta Longhini2, Michael C. Welle 2, Guillem Alenyà1, Danica Kragic2 and Júlia Borràs1
1 Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain.
2 Robotics, Perception and Learning Lab, KTH Royal Isntitute of Technology, Stockholm, Sweden.
Abstract - The field of robotics faces inherent challenges in manipulating deformable objects, particularly in understanding and standardizing fabric properties like elasticity, stiffness, and friction. While the significance of these properties is evident in the realm of cloth manipulation, accurately categorising and comprehending them in real-world applications remains elusive. This study sets out to address two primary objectives: (1) to provide a framework suitable for robotics applications to characterise cloth objects, and (2) to study how these properties influence robotic manipulation tasks. Our preliminary results validate the framework’s ability to characterise cloth properties and compare cloth sets, and reveal the influence that different properties have on the result of some manipulation primitives. We believe that, in general, results on the manipulation of clothes should be reported along with a better description of the garments used in the evaluation.
We provide a step-by-step of the measurement systems to quantify the properties of cloth objects.
We provide some templates to facilitate the application of the measurement systems, including a printable ruler for measuring the elasticity and friction and an Aruco pattern to measure the stiffness.
The code to measure the stiffness of clothes can be found on the Github page.
STIFFNESS


FRICTION


ELASTICITY


The measurement systems can be used, in addition to characterising individual objects, to compare and evaluate different sets of cloth through the quantity of variability that they provide in each property. This can serve to imrpove the creation of a cloth set that suits its purpose. To do so, it is necessary to take measures of the abovementioned properties of all the cloth objects in the set and represent the variability in a radar chart computing the difference between the maximum and minimum values of each property.
This has been done for three existing cloth set used in the cloth manipulation literature. The radar chart for each cloth sets are shown in the corresponding links.
Click images to see comparison
EOS: A. Longhini, M. Moletta, A. Reichlin, M. C. Welle, D. Held, Z. Erickson and D. Kragic, "Edo-net: Learning elastic properties of deformable objects from graph dynamics", in IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 3875-3881.
HCOS: I. Garcia-Camacho, J. Borràs, B- Calli, A. Norton and G. Alenyà", "Household cloth object set: Fostering benchmarking in deformable object manipulation", in IEEE Robotics and Automation Letters, 2022, vol. 7, no. 3, pp. 5866-5873.
DOS: O. Gustavsson, T. Ziegler, M. Welle, J. Bütepage, A. Varava and D. Kragic, "Cloth manipulation based on category classification and landmark detection", in International Journal of Advanced Robotic Systems, 2022.
I. Garcia-Camacho, A. Longhini, M.C. Welle, G. Alenyà, D. Kragic and J. Borràs, "Standardization of cloth objects and its relevance in robotic manipulation", in IEEE International Conference on Robotics and Automation, 2024
Send any comments or questions to Irene at: igarcia@iri.upc.edu