Abstract
This paper explores a novel approach to model strategies for flattening wrinkled cloth learning from humans. A human participant study was conducted where the participants were presented with various wrinkle types and tasked with flattening the cloth using the fewest actions possible. A camera and Aruco marker were used to capture images of the cloth and finger movements, respectively. The human strategies for flattening the cloth were modeled using a supervised regression neural network, where the cloth images served as input and the human actions as output. Before training the neural network, a series of image processing techniques were applied, followed by Principal Component Analysis (PCA) to extract relevant features from each image and reduce the input dimensionality. This reduction decreased the model's complexity and computational cost. The actions predicted by the neural network closely matched the actual human actions on an independent data set, demonstrating the effectiveness of neural networks in modeling human actions for flattening wrinkled cloth.
Recommended Citation
N. Kant et al., "Modeling Human Strategy for Flattening Wrinkled Cloth using Neural Networks," Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, pp. 673 - 678, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/SMC54092.2024.10832048
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Aruco marker; flattening cloth; human strategy; human-machine cooperation; learning; neural network; wrinkle
International Standard Book Number (ISBN)
978-166541020-5
International Standard Serial Number (ISSN)
1062-922X
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Comments
Michigan State University, Grant CMMI-2326227