ICRA 2023poster10 citations

Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Elastic Context: Encoding Elasticity for Data-driven Models of Textiles

Alberta Longhini, Marco Moletta, Alfredo Reichlin, Michael C. Welle, Alexander Kravberg, Yufei Wang, David Held, Zackory Erickson

Abstract

Physical interaction with textiles, such as assistive dressing or household tasks, requires advanced dexterous skills. The complexity of textile behavior during stretching and pulling is influenced by the material properties of the yarn and by the textile's construction technique, which are often unknown in real-world settings. Moreover, identification of physical properties of textiles through sensing commonly available on robotic platforms remains an open problem. To address this, we introduce Elastic Context (EC), a method to encode the elasticity of textiles using stress-strain curves adapted from textile engineering for robotic applications. We employ EC to learn generalized elastic behaviors of textiles and examine the effect of EC dimension on accurate force modeling of real-world non-linear elastic behaviors.

BibTeX
@inproceedings{icra2023_elasticcontexten,
  title = {Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Elastic Context: Encoding Elasticity for Data-driven Models of Textiles},
  author = {Alberta Longhini and Marco Moletta and Alfredo Reichlin and Michael C. Welle and Alexander Kravberg and Yufei Wang and David Held and Zackory Erickson and Danica Kragic},
  booktitle = {ICRA 2023},
  year = {2023}
}
Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Elastic Context: Encoding Elasticity for Data-driven Models of Textiles · ICRA 2023