# Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology
## 4 Smart Textile  
In order to solve the problems associated with using vision for the state estimation of cloth, we developed an accessible, low-cost smart textile that can identify its actual pose. By integrating a flexible tactile sensor grid into a flat textile piece, a pose estimation model can be trained. Consequently, the textile is able to sense and react to its surrounding environment, making it self-aware or making it an active smart textile [29].  
The layers in the smart textile can be seen in Figure 2. The textile consists of piezoresistive rubber (commercially available as Velostat(r)) that is sandwiched between two layers of conductive threads that act as electrodes [30]. The electrodes are made of copper strips that are insulated with a polyimide film. This results in a matrix of 12 by 7 tactile cells. The signal conditioning for measuring the pressure applied over a tactile cell is based on the voltage divider principle. More specifically, the voltage is measured between the tactile cell, which acts as a variable resistor, and a fixed resistor of 5.6 kOhm. The voltage was measured and processed by a standard microcontroller. The resulting textile can be seen in Figure 3.  
Based on the output of the 12 by 7 tactile cells, a linear classifier is trained to predict the garment state: folded or unfolded. Therefore, we first collected data from the tactile cells by bringing the cloth into multiple configurations while hand-labeling the associated state. We define the folded state of the cloth with some slack because of the way in which the robots are positioned in order to avoid a collision; this is illustrated in Figure 4. The collected data were split into a training set of 1418 samples and a test set of 511 samples. In Figure 5, the average voltage and standard deviation of the tactile cells for both garment states are shown. One can observe that there is a clear voltage peak in the center column, implying that the tactile cells in the center of the textile experience higher pressure due to the folded state.  
Figure 2: Exploded-view drawing of the textile with integrated tactile sensing. Conductive threads organized in a 12 by 7 grid are applied on piezoresistive rubber, creating a variable resistor. The resulting voltage acts as a proxy for local forces applied on the cloth.  
Figure 4: Example of the folding task on the real platform. The robotic arms are positioned in such a way that they cannot collide, making it physically impossible to fold the cloth fully. Thus, we do not require a fully folded state of the textile from the agent.  
Figure 3: Our resulting implementation of a smart textile which is able to detect and translate local forces to a state configuration (for example, folded or unfolded). The smart textile is made using accessible off-the-shelf materials and tools.  
The classifier was trained using logistic regression with l2-norm regularization on 1418 training samples in a five-fold cross-validation setting. The regularization parameter was optimized by performing a random search and by using the F1-score as a performance metric. In order to verify the usability of the classifier, we tested it on an unseen test set of 511 samples which resulted in an average F1-score of 0.97. The full results of the test set are summarized in Table 1.  
We are able to classify different shape configurations for the textile piece with high accuracy, making the smart textile useful for learning purposes. Other desired features such as wrinkle detection are hard to measure with this technology given the limited sensitivity of the piezoresistive rubber used.