## 3 Materials and Methods
### Data Collection  
The conducted experiments were performed using a real-world UR5e, a 6 degrees of freedom (DoF) collaborative robotic manipulator. The robot was mounted on the platform and fixed (bolted) on a flat surface (i.e., aluminium table). Furthermore, in this experiment, along with the proposed force platform based on strain gauges, the 6D force-torque sensor  
Figure 1: The conceptual presentation of a base force measurement platform with multiple strain gauges. (**a**) Strain gauge. (**b**) Possible placement of four strain gauges in regards to the platform scenarios (the right one was used in the experiment). (**c**) Practical realisation of the left concept from subfigure (**b**). (JR3 model 45E) was used in order to compare the quality of the obtained results using two different force-sensing devices on the same dataset. In all experiments, data obtained from the UR5e robot and its controller (e.g., joint positions, joint velocities, tool forces/torques, joint motor currents) [33] were used as ground truth for the neural network training and are referred to hereafter as _measured_ values. These data are often used as the ground truth for different applications in the literature [4; 34]. The experimental setup for this experiment is shown in Figure 2, while the close-up view of the force sensing setup is given in Figure 3 (where the aluminium table was removed by image-processing tool, for clarity reasons). As can be seen from the figures, the whole sensor setup has a sandwich-like structure where strain gauges are fixed to a flat aluminium table via small pieces of aluminium blocks on one side (in order to be elevated and, thus, function properly), and to the lower base plate (marked \"Strain gauge platform\") on the other side. On top of the lower base plate, the JR3 sensor is mounted with the upper base plate mounted on top of the JR3 sensor. Finally, on top of the upper base plate, the UR5e robot manipulator was securely mounted. All mounting was performed using nuts and bolts of appropriate diameters. It should be noted that, due to individual hardware component dimensions and shapes (6D force-torque sensor and strain gauge platform), this was the only viable way of connecting them. However, this configuration has a disadvantage in that, due to small strain gauge deformations during the measurement, the platform can move slightly, affecting 6D sensor measurement, since its bottom part is connected to it. Thus, this should be kept in mind when interpreting and comparing obtained results. Finally, since the 6D sensor was placed on top of the strain gauge platform, it had an effect on the robot model implicitly learned by the network (i.e., the same network would not work if the sensor was removed and the robot placed directly on the platform). This additional complexity added to neural network learning can, in our view, have only negative effects on accuracy, thus making the obtained results a kind of worst-case scenario for the strain gauge platform. The approach to data collection was the same in both experiments and was conducted as follows. The robot was programmed to execute random motions using UR Script and PolyScope software. First, a random valid goal was chosen (via random joint angles for all manipulator joints within the manufacturer-specified angle ranges) and the trajectory from the current robot pose to the goal robot pose was planned and then executed. If the random goal was not a valid one (for example, generating a trajectory that results in self-collision) it was discarded, and a new random one was generated from the start. Additionally, targeted maximal joint speed and acceleration (as the percentage of maximal allowed joint velocity/acceleration) were selected from the safe operational range (i.e., high velocities and accelerations were not selected due to robot and human experimenter safety, and since they are not normally used during human-robot interaction [35]). Note that joint velocities were programmed to finalise their movements all at the same time.