RT Journal Article
T1 Enabling garment-agnostic laundry tasks for a Robot Household Companion
A1 Estévez Fernández, David
A1 González Víctores, Juan Carlos
A1 Fernández Fernández, Raúl
A1 Balaguer Bernaldo de Quirós, Carlos
AB Domestic chores, such as laundry tasks, are dull and repetitive. These tasks consume a significant amount of daily time, and are however unavoidable. Additionally, a great portion of elder and disabled people require help to perform them due to lack of mobility. In this work we present advances towards a Robot Household Companion (RHC), focusing on the performance of two particular laundry tasks: unfolding and ironing garments. Unfolding is required to recognize the garment prior to any later folding operation. For unfolding, we apply an interactive algorithm based on the analysis of a colored 3D reconstruction of the garment. Regions are clustered based on height, and a bumpiness value is computed to determine the most suitable pick and place points to unfold the overlapping region. For ironing, a custom Wrinkleness Local Descriptor (WiLD) descriptor is applied to a 3D reconstruction to find the most significant wrinkles in the garment. These wrinkles are then ironed using an iterative path-following control algorithm that regulates the amount of pressure exerted on the garment. Both algorithms focus on the feasibility of a physical implementation in real unmodified environments. A set of experiments to validate the algorithms have been performed using a full-sized humanoid robot.
PB Elsevier
SN 0921-8890
YR 2020
FD 2020-01
LK https://hdl.handle.net/10016/33108
UL https://hdl.handle.net/10016/33108
LA eng
NO This work was supported by RoboCity2030-III-CM project (S2013/MIT-2748), funded by Programas de Actividades I+D in Comunidad de Madrid, Spain and EU and by a FPU grant funded by Ministerio de Educación, Cultura y Deporte, Spain. It was also supported by the anonymous donor of a red hoodie used in our initial trials. We gratefully acknowledge the support of NVIDIA, United States Corporation with the donation of the NVIDIA Titan X GPU used for this research.
DS e-Archivo
RD 1 sept. 2024