论文标题
在模拟中学习触摸感：一种基于视觉触觉感应的SIM模拟策略
Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
论文作者
论文摘要
数据驱动的触觉传感方法旨在克服与软材料进行准确建模接触的复杂性。但是，对数据效率的担忧以及应用于各种任务时的概括能力会损害它们的广泛采用。本文侧重于基于视觉的触觉传感器，该方面旨在重建在其软表面上应用的三维接触力的分布。通过各自域中的最新技术得出的软材料和摄像机投影的精确模型，用于在模拟中生成数据集。提出了一种完全从模拟数据训练量身定制的深神经网络的策略。所得的学习体系结构可直接在多个触觉传感器上转移，而无需进一步的训练，并且可以对真实数据进行准确的预测，同时显示出有希望的概括能力，可以看不见接触条件。
Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by concerns about data efficiency and the capability to generalize when applied to various tasks. This paper focuses on both these aspects with regard to a vision-based tactile sensor, which aims to reconstruct the distribution of the three-dimensional contact forces applied on its soft surface. Accurate models for the soft materials and the camera projection, derived via state-of-the-art techniques in the respective domains, are employed to generate a dataset in simulation. A strategy is proposed to train a tailored deep neural network entirely from the simulation data. The resulting learning architecture is directly transferable across multiple tactile sensors without further training and yields accurate predictions on real data, while showing promising generalization capabilities to unseen contact conditions.
