论文标题
用于光学触觉传感的SIM转交付转移
Sim-to-Real Transfer for Optical Tactile Sensing
论文作者
论文摘要
深度学习和强化学习方法已被证明可以学习灵活且复杂的机器人控制器。但是，对大量培训数据的依赖通常需要在模拟中进行数据收集，近年来正在开发许多SIM到现实转移方法。在本文中，我们使用Tactip光学触觉传感器研究了这些技术，用于触觉传感，该技术由可变形的尖端组成，摄像头观察了该尖端内引脚的位置。我们设计了一个用于软体模拟的模型，该模型是使用Unity Physics引擎实施的，并训练了一个神经网络，以预测与传感器接触时边缘的位置和角度。使用域随机化技术进行SIM到现实传输，我们展示了如何使用该框架在现实世界测试中使用小于1 mm的预测误差来准确预测边缘，而没有任何现实世界数据。
Deep learning and reinforcement learning methods have been shown to enable learning of flexible and complex robot controllers. However, the reliance on large amounts of training data often requires data collection to be carried out in simulation, with a number of sim-to-real transfer methods being developed in recent years. In this paper, we study these techniques for tactile sensing using the TacTip optical tactile sensor, which consists of a deformable tip with a camera observing the positions of pins inside this tip. We designed a model for soft body simulation which was implemented using the Unity physics engine, and trained a neural network to predict the locations and angles of edges when in contact with the sensor. Using domain randomisation techniques for sim-to-real transfer, we show how this framework can be used to accurately predict edges with less than 1 mm prediction error in real-world testing, without any real-world data at all.
