## 3 Experimental Setup & Procedure for Texture Discrimination
### Experimental Procedure  
Figure 4 shows the flow chart describing the experiment performed using the texture discrimination platform to discriminate between texture samples. The experiment was divided into a training mode and a texture discrimination mode. The training mode was a learning step, and the texture discrimination mode was a step in which arbitrary samples were discriminated based on the learned data. The method of operation was as follows. First, the positions of the motion stages (X-axis stage and Z-axis stage) were initialized before conducting measurements under each mode. In the case of the training mode, the number of measurement repetitions was inputted. The averages and standard deviations of each of the four tactile modalities could be obtained through repeated measurements. The error of the average value decreases as the number of repetitions increases; thus, we conducted 10 repeated measurements to sufficiently minimize the error of the average values. Next, to ensure the robot fingertip module approached the texture sample in a stable manner, the Z-axis stage, which has a displacement sensor, was set to approach the sample in two phases. In the first phase, the relative positions of the sample to be measured and the module was determined using the displacement sensor, and this information was sent to the stage as feedback. The stage then rapidly closed the distance between the module and the sample (Fast approach). Afterwards, the module approached the texture sample until the moment of contact at a slow, constant speed (Slow approach). When the tactile sensor in the module detected contact force, this signal was fed back to the stage to halt the movement. Once the module and sample came into contact in this manner, the static contact phase commenced. In this phase, the Z-axis stage moved in steps to measure the hardness and thermal conductivity of the sample. First, the module moved downwards in the vertical (Z-axis) direction by a total displacement of 1 mm over eight equal steps, and the contact force was measured for each step. Through this method, the hardness of the sample could be estimated by measuring the difference in contact force between samples for a given displacement. After indenting to a depth of 1 mm, the contact was maintained for 15 s to detect changes in the temperature of the module over time as heat flowed from the module to the sample. This information could be used to obtain a value representative of the thermal conductivity of the sample. The static contact phase was completed by completing the aforementioned sequence of actions. For the following dynamic contact phase, the X-axis stage was moved at a constant speed (10 mm/s) for 10 s to scan the surface of the sample. This process provided data that could be used to measure the friction coefficient and roughness of the texture sample. Lastly, the tactile sensor signals obtained from each phase were analyzed in the time and frequency domains. Therefore, in the training mode, repeated measurements were conducted for each sample to analyze  
Figure 3: Schematic diagram of the motorized texture discrimination platform with the sensor circuit board for processing multimodal tactile sensor signals that are derived from static and dynamic contact. (Inset) Block diagram of a sensor circuit board.  
the obtained sensor signals with specific formulas, and the obtained data (average and standard deviation) were saved on a database. For the texture discrimination mode, a specific sample was measured and the analyzed data were compared to the saved database to discriminate which sample most closely resembles the texture sample being measured using a specific algorithm (described in detail in Section 4). The overall process was controlled using the LabVIEW software.