用神经网络学习人类抚平褶皱的动作策略，效果接近真人。
Modeling Human Strategy for Flattening Wrinkled Cloth Using Neural Networks
- 通过摄像头和标记点捕捉人类抚平布料的动作，输入图像输出动作。
- 主成分分析降低图像维度，使模型更轻量高效。
- 预测动作与真人操作高度一致，适合人机协作场景。
本文探索了一种从人类行为中学习抚平褶皱布料新策略的方法。通过人类参与者实验，让参与者在面对不同褶皱类型时，以最少动作完成抚平任务。使用相机和Aruco标记捕捉布料图像和手指运动。采用监督回归神经网络建模人类抚平策略，以布料图像为输入，人类动作为输出。训练前对图像进行一系列处理，并通过主成分分析（PCA）提取关键特征，降低输入维度，从而减少模型复杂度和计算成本。神经网络预测的动作在独立数据集上与真实人类动作高度吻合，证明了神经网络在建模人类抚平动作上的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。