论文标题
TATO：用于基于高分辨率的触觉传感器的快速，灵活和开源模拟器
TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
论文作者
论文摘要
模拟器在原型制作，调试和基准测试机器人技术和控制方面的新进步中发挥了重要作用。尽管存在许多物理引擎，但是现实世界的某些方面比其他方面更难模拟。到目前为止，精确模拟的一个方面之一是触摸感应。为了解决这一差距，我们提出了TARTO-一种快速，灵活且开源的模拟器，用于基于视觉的触觉传感器。该模拟器允许以每秒数百帧的形式渲染现实的高分辨率触摸读数，并且可以轻松配置以模拟不同的基于视觉的触觉传感器，包括数字和杂物。在本文中，我们详细介绍了推动触觉实施以及如何在其建筑中反映的原则。我们通过学习使用100万个掌握的触摸以及大理石操纵控制任务来预测掌握稳定性来证明触觉。此外，我们提供了概念验证，即可以成功地用于SIM2REAL应用程序。我们认为，TACO是在机器人应用中广泛采用触摸感的一步，并使对多模式学习和控制感兴趣的机器学习从业者能够进行触摸感。 TATO是https://github.com/facebookresearch/tacto的开放源。
Simulators perform an important role in prototyping, debugging, and benchmarking new advances in robotics and learning for control. Although many physics engines exist, some aspects of the real world are harder than others to simulate. One of the aspects that have so far eluded accurate simulation is touch sensing. To address this gap, we present TACTO - a fast, flexible, and open-source simulator for vision-based tactile sensors. This simulator allows to render realistic high-resolution touch readings at hundreds of frames per second, and can be easily configured to simulate different vision-based tactile sensors, including DIGIT and OmniTact. In this paper, we detail the principles that drove the implementation of TACTO and how they are reflected in its architecture. We demonstrate TACTO on a perceptual task, by learning to predict grasp stability using touch from 1 million grasps, and on a marble manipulation control task. Moreover, we provide a proof-of-concept that TACTO can be successfully used for Sim2Real applications. We believe that TACTO is a step towards the widespread adoption of touch sensing in robotic applications, and to enable machine learning practitioners interested in multi-modal learning and control. TACTO is open-source at https://github.com/facebookresearch/tacto.
