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.
翻译:模拟器在原型设计、调试和将机器人的新进步与控制学习基准化方面起着重要作用。 尽管存在许多物理引擎, 但真实世界的某些方面比其它方面更难模拟。 迄今无法进行准确模拟的一个方面是触摸感测。 为了弥补这一差距, 我们展示了TACTO- 一个快速、 灵活和开源的视觉触动传感器模拟器。 这个模拟器可以使现实的高分辨率触摸读数达到每秒数百个框架, 并且可以很容易地配置为模拟基于视觉的不同触动传感器, 包括DIGIT和OmniTact。 在本文中, 我们详细介绍了推动TACTO实施的原则以及这些原则如何反映在其结构中。 我们展示了TACTO的感知性任务, 学会如何利用100万个握手的触觉来预测稳定性, 以及大理学操纵控制任务。 此外, 我们提供了一种证据, 证明TACTO可以成功地用于Sim2Real应用。 我们相信, TACTOTO是推动广泛感触摸应用的开放操作者和多感应操作者。 我们相信, TACTOTOTA- 学习机操作者在广泛感化应用中可以学习。