Modeling Human Strategy for Flattening Wrinkled Cloth Using Neural Networks

TL;DR

This paper presents a neural network-based model trained on human demonstrations to predict strategies for efficiently flattening wrinkled cloth, reducing actions and capturing human-like behavior.

Contribution

It introduces a supervised neural network approach combined with PCA for modeling human strategies in cloth flattening tasks.

Findings

Neural network accurately predicts human actions.

PCA reduces input complexity effectively.

Model closely matches human strategies.

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.