# Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors

arXiv: https://arxiv.org/abs/2503.09100 · HTML: https://mlsanity.com/paper/2503.09100/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks](https://mlsanity.com/paper/2112.03227/) (2021)
- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors](https://mlsanity.com/paper/2012.08456/) (2020)
- [Cable Manipulation with a Tactile-Reactive Gripper](https://mlsanity.com/paper/1910.02860/) (2019)
- [Taxim: An Example-based Simulation Model for GelSight Tactile Sensors](https://mlsanity.com/paper/2109.04027/) (2021)
- [Simulation of Vision-based Tactile Sensors using Physics based Rendering](https://mlsanity.com/paper/2012.13184/) (2020)
- [Generation of GelSight Tactile Images for Sim2Real Learning](https://mlsanity.com/paper/2101.07169/) (2021)
- [Ground truth force distribution for learning-based tactile sensing: a finite element approach](https://mlsanity.com/paper/1909.04000/) (2019)
- [Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors](https://mlsanity.com/paper/2301.08343/) (2023)
- [Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard](https://mlsanity.com/paper/2008.02646/) (2020)
- [FOTS: A Fast Optical Tactile Simulator for Sim2Real Learning of Tactile-motor Robot Manipulation Skills](https://mlsanity.com/paper/2404.19217/) (2024)
- [Simulation of Optical Tactile Sensors Supporting Slip and Rotation using Path Tracing and IMPM](https://mlsanity.com/paper/2405.02914/) (2024)
- [Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks](https://mlsanity.com/paper/2408.08276/) (2024)

## Built on by (most cited first, 4 of 4)

- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment](https://mlsanity.com/paper/2603.28475/) (2026)
- [Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception](https://mlsanity.com/paper/2605.05053/) (2026)
- [Bridging Language and Spherical Space: Object-Centric Control for Text-to-Panorama Generation](https://mlsanity.com/paper/2608.20691/) (2026)

## Abstract (arXiv)

With the development of robotics technology, some tactile sensors, such as vision-based sensors, have been applied to contact-rich robotics tasks. However, the durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition. Utilizing simulation to generate tactile data has emerged as a reliable approach to address this issue. While data-driven methods for tactile data generation lack robustness, finite element methods (FEM) based approaches require significant computational costs. To address these issues, we integrated a pinhole camera model into the low computational cost vision-based tactile simulator Tacchi that used the Material Point Method (MPM) as the simulated method, completing the simulation of marker motion images. We upgraded Tacchi and introduced Tacchi 2.0. This simulator can simulate tactile images, marked motion images, and joint images under different motion states like pressing, slipping, and rotating. Experimental results demonstrate the reliability of our method and its robustness across various vision-based tactile sensors.