Título del libro:
Título del capítulo: Prediction of Deformations on Elastic Objects Using an LSTM Model
VERONICA ESTHER ARRIOLA RIOS;
Autores externos:
Idioma:
Año de publicación:
2024
Palabras clave:
Mobile robots; Prediction models; Robotic arms; 2D deformation; 2d deformation registration; Deformable object; Deformation's predictions; Learning of deformation; Long short-term memory network; Memory network; Short term memory; Volumetric deformable object; Volumetrics; Long short-term memory
Resumen:
A volumetric deformable object does not have a fixed representation. Its contour changes as forces are applied on it. The number of distinctive traits like corners or points of high curvature are modified and the level of detail required for its description varies as its shape becomes simple or more intricate. Furthermore, the type of deformation it will undergo varies drastically depending on its material and original shape. For these reasons mobile robots face a challenging problem when on the need of manipulating everyday objects like toys, cushions or dishwashing sponges. In this paper we propose a Long Short-Term Memory (LSTM) neural network that learns to predict the deformation of the contour of an elastic object, taking as input the original contour and the sequence of position and force measurements of the robotic finger that interacts with it. Even though the representation of the contour we use is still of fixed length, our model makes reasonable predictions of deformation for interactions it has not seen before for the entire interaction and not only for the next frame as other models in the literature do. Also, since it uses only local information of pieces of the object, we envision that this work can be extended to cover the case of representations of varying length. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Entidades citadas de la UNAM:
ISBN:
9783031713590
Editorial:
Springer Science and Business Media Deutschland GmbH
