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.

翻译：触觉传感为增强当今机器人的交互能力提供了一个前景广阔的途径。BioTac是一种常用的触觉传感器，能使机器人感知并响应物理触觉刺激。然而，该传感器的非线性特性为其行为仿真带来了挑战。本文首先研究了一种利用温度、力和接触点位置来预测传感器输出的BioTac仿真模型。我们发现，使用BioTac温度读数进行训练无法在部署阶段获得准确的传感器输出预测。因此，我们测试了三种替代模型，即XGBoost回归器、神经网络和Transformer编码器。我们在不依赖温度读数的情况下训练这些模型，并对输入向量的窗口大小进行了详细研究。实验证明，我们的方法相较于基线网络取得了统计学上显著的改进。此外，我们的结果表明，在此任务中，XGBoost回归器和Transformer模型的表现优于传统的前馈神经网络。我们已将全部代码和结果在线发布于 https://github.com/wzaielamri/Optimizing_BioTac_Simulation。