Generation of GelSight Tactile Images for Sim2Real Learning

TL;DR

This paper introduces a novel method to simulate GelSight tactile sensors in Gazebo, enabling the use of tactile data in robotic Sim2Real learning to overcome vision occlusion issues.

Contribution

The paper presents the first simulation of GelSight tactile sensors in Gazebo, providing high-resolution tactile images for improved robotic manipulation learning.

Findings

Simulated GelSight sensor produces realistic tactile images

Enables tactile sensing in Sim2Real robotic tasks

Potential to improve manipulation under occlusion

Abstract

Most current works in Sim2Real learning for robotic manipulation tasks leverage camera vision that may be significantly occluded by robot hands during the manipulation. Tactile sensing offers complementary information to vision and can compensate for the information loss caused by the occlusion. However, the use of tactile sensing is restricted in the Sim2Real research due to no simulated tactile sensors being available. To mitigate the gap, we introduce a novel approach for simulating a GelSight tactile sensor in the commonly used Gazebo simulator. Similar to the real GelSight sensor, the simulated sensor can produce high-resolution images by an optical sensor from the interaction between the touched object and an opaque soft membrane. It can indirectly sense forces, geometry, texture and other properties of the object and enables Sim2Real learning with tactile sensing. Preliminary experimental results have shown that the simulated sensor could generate realistic outputs similar to the ones captured by a real GelSight sensor. All the materials used in this paper are available at https://danfergo.github.io/gelsight-simulation.