Authors:
Georgies Tzelepis
1
;
Eren Aksoy
2
;
Júlia Borràs
1
and
Guillem Alenyà
1
Affiliations:
1
Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Llorens i Artigas 4-6, 08028 Barcelona, Spain
;
2
Halmstad University, Center for Applied Intelligent Systems Research, Halmstad, Sweden
Keyword(s):
Robotic Perception, Garment Manipulation, Semantics, Cloth, Transfer Learning, Domain Adaptation.
Abstract:
Deformable object manipulations, such as those involving textiles, present a significant challenge due to their high dimensionality and complexity. In this paper, we propose a solution for estimating semantic states in cloth manipulation tasks. To this end, we introduce a new, large-scale, fully-annotated RGB image dataset of semantic states featuring a diverse range of human demonstrations of various complex cloth manipulations. This effectively transforms the problem of action recognition into a classification task. We then evaluate the generalizability of our approach by employing domain adaptation techniques to transfer knowledge from human demonstrations to two distinct robotic platforms: Kinova and UR robots. Additionally, we further improve performance by utilizing a semantic state graph learned from human manipulation data.