  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.
