Simulation is widely used in robotics for system verification and large-scale
data collection. However, simulating sensors, including tactile sensors, has
been a long-standing challenge. In this paper, we propose Taxim, a realistic
and high-speed simulation model for a vision-based tactile sensor, GelSight. A
GelSight sensor uses a piece of soft elastomer as the medium of contact and
embeds optical structures to capture the deformation of the elastomer, which
infers the geometry and forces applied at the contact surface. We propose an
example-based method for simulating GelSight: we simulate the optical response
to the deformation with a polynomial look-up table. This table maps the
deformed geometries to pixel intensity sampled by the embedded camera. In order
to simulate the surface markers' motion that is caused by the surface stretch
of the elastomer, we apply the linear elastic deformation theory and the
superposition principle. The simulation model is calibrated with less than 100
data points from a real sensor. The example-based approach enables the model to
easily migrate to other GelSight sensors or its variations. To the best of our
knowledge, our simulation framework is the first to incorporate marker motion
field simulation that derives from elastomer deformation together with the
optical simulation, creating a comprehensive and computationally efficient
tactile simulation framework. Experiments reveal that our optical simulation
has the lowest pixel-wise intensity errors compared to prior work and can run
online with CPU computing. Our code and supplementary materials are
open-sourced at this https URL

本文提出了一种基于仿生视觉的触觉传感器 GelSight 的仿真模型 Taxim，通过实现多项式查询表和线弹性变形原理来模拟其光学响应和表面标记的运动。实验结果表明，Taxim 的像素误差最小，可在 CPU 计算下在线运行。