Tactile sensors are believed to be essential in robotic manipulation, and prior works often rely on experts to reason the sensor feedback and design a controller. With the recent advancement in data-driven approaches, complicated manipulation can be realised, but an accurate and efficient tactile simulation is necessary for policy training. To this end, we present an approach to model a commonly used pressure sensor array in simulation and to train a tactile-based manipulation policy with sim-to-real transfer in mind. Each taxel in our model is represented as a mass-spring-damper system, in which the parameters are iteratively identified as plausible ranges. This allows a policy to be trained with domain randomisation which improves its robustness to different environments. Then, we introduce encoders to further align the critical tactile features in a latent space. Finally, our experiments answer questions on tactile-based manipulation, tactile modelling and sim-to-real performance.

翻译：触觉传感器被认为在机器人操作中至关重要，以往的研究通常依赖专家解读传感器反馈并设计控制器。随着数据驱动方法的进步，复杂操作得以实现，但策略训练需要精确高效的触觉仿真。为此，我们提出一种在仿真中建模常用压力传感器阵列的方法，并基于仿真到现实的迁移思想训练触觉操作策略。模型中每个触觉单元被表示为质量-弹簧-阻尼系统，其参数通过迭代方式被识别为合理范围。这使得策略能够通过域随机化进行训练，从而增强对不同环境的鲁棒性。随后，我们引入编码器以进一步对齐潜在空间中的关键触觉特征。最终，我们的实验回答了关于基于触觉的操作、触觉建模以及仿真到现实性能的关键问题。