Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today's robots. BioTac is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli. However, the sensor's non-linearity poses challenges in simulating its behavior. In this paper, we first investigate a BioTac simulation that uses temperature, force, and contact point positions to predict the sensor outputs. We show that training with BioTac temperature readings does not yield accurate sensor output predictions during deployment. Consequently, we tested three alternative models, i.e., an XGBoost regressor, a neural network, and a transformer encoder. We train these models without temperature readings and provide a detailed investigation of the window size of the input vectors. We demonstrate that we achieve statistically significant improvements over the baseline network. Furthermore, our results reveal that the XGBoost regressor and transformer outperform traditional feed-forward neural networks in this task. We make all our code and results available online on https://github.com/wzaielamri/Optimizing_BioTac_Simulation.

通过温度、力量和接触点位置对BioTac进行模拟，训练了不包括温度读数的三个替代模型（XGBoost回归器、神经网络和Transformer编码器），并研究了输入向量的窗口大小，实验结果表明XGBoost回归器和Transformer在这一任务中优于传统的前馈神经网络。

优化BioTac模拟的真实触觉知觉