## 5 Conclusions  
This paper presented a new, generic platform as an alternative to more expensive 6D force-torque sensors, which uses multiple 1D strain gauges instead. Then, the platform was used for robot end-effector force estimation and joint-side torque estimation, demonstrating good performance. The data were collected on the real-world robot to train neural networks for these two tasks. Since the experimental setup included both the custom-built platform and force-torque sensor, it was possible to directly compare the obtained neural networks using one source against the other, which demonstrated that networks trained with data from stain gauges performed marginally better on the same dataset.  
Therefore, it might be concluded that the proposed force-measuring platform works well when used in applications where there is a need to cost-efficiently estimate either end-effector forces or joint-side torques (or both) while freeing up the robot's payload. Compared to some similar research from the literature (like [32]) our approach does not require any calibration, specialised hardware, or knowledge of accurate robot models. However, it does rely on robot-specific neural networks. This network is obtained through neural network training, which requires large amounts of data and significant effort to collect it. However, this effort can be reduced by learning from simulation (for a particular  
Figure 8: Examples of the joint torque estimation performance of the two systems using a real-world URSe robotic manipulator.  
robot, as was demonstrated in [43]) and later applying transfer learning principles to a smaller amount of real-world data.  
In the future, this work will be extended to take into account the potential slope when the robot is not mounted on a flat surface, or more realistically, when the robot is mounted on a mobile platform for applications in real-world outdoor scenarios. One step of this was already achieved in our previous work [43] (only in simulation and only with the force-torque sensor, not the proposed platform based on strain gauges), but in the future, we plan to conduct the real-world experiment both with force sensors and with the platform. Additionally, the generic platform will be augmented with additional 1D strain gauges to explore if this contributes to the accuracy of the method.  
Conceptualisation, S.K. and J.M.; formal analysis, S.K., J.M. and R.K.; methodology, S.K., J.M. and R.K.; software, S.K.; supervision, V.P.; validation, J.M.; visualisation, S.K.; writing--original draft, S.K.; writing--review and editing, J.M., R.K. and V.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Authors would like to express gratitude to [PERSON] for his patience in discussing the topic and giving useful pointers in the analysis.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
1D 1-dimensional  
2D 2-dimensional  
3D 3-dimensional  
6D 6-dimensional  
A/D  Analog-to-Digital  
CNN  Convolutional Neural Network  
DeLaN  Deep Lagrangian Network  
DoF  Degrees of Freedom  
ELU  Exponential Linear Unit  
LNN  Lagrangian Neural network  
LSTM  Long-short Term Memory  
MLP  Multilayer Perceptron  
MSE  Mean Square Error  
NRMSE  Normalised Root Mean Square Error  
ReLU  Rectified Linear Unit  
RMSE  Root Mean Square Error