Publication
Semantic state estimation in robot cloth manipulations using domain adaptation from human demonstrations
Conference Article
Conference
International Conference on Computer Vision Theory and Applications (VISAPP)
Edition
19
Pages
172-182
Doc link
http://dx.doi.org/10.5220/0012368200003660
File
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.
Categories
image classification.
Author keywords
Domain Adaptation, Semantic States, Deformable Objects
Scientific reference
G. Tzelepis, A. Eren Erdal, J. Borràs and G. Alenyà. Semantic state estimation in robot cloth manipulations using domain adaptation from human demonstrations, 19 International Conference on Computer Vision Theory and Applications, 2024, Rome, pp. 172-182.
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