Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors
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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.
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
13 of 28 references.
References · 13
- 2021CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Taskscited by 584
- 2024Binding Touch to Everything: Learning Unified Multimodal Tactile Representationscited by 95
- 2020TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensorscited by 63
- 2019Cable Manipulation with a Tactile-Reactive Grippercited by 46
- 2021Taxim: An Example-based Simulation Model for GelSight Tactile Sensorscited by 43
- 2020Simulation of Vision-based Tactile Sensors using Physics based Renderingcited by 15
- 2021Generation of GelSight Tactile Images for Sim2Real Learningcited by 15
- 2019Ground truth force distribution for learning-based tactile sensing: a finite element approachcited by 8
- 2023Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensorscited by 8
- 2020Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboardcited by 7
- 2024FOTS: A Fast Optical Tactile Simulator for Sim2Real Learning of Tactile-motor Robot Manipulation Skillscited by 6
- 2024Simulation of Optical Tactile Sensors Supporting Slip and Rotation using Path Tracing and IMPMcited by 2
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Citing papers per year: 2026: 4.
4 papers.
- 2026AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perceptioncited by 12
- 2026Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deploymentcited by 1
- 2026Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perceptioncited by 0
- 2026Bridging Language and Spherical Space: Object-Centric Control for Text-to-Panorama Generationcited by 0
- 2026Bridging Language and Spherical Space: Object-Centric Control for Text-to-Panorama Generation21 Aug 2026
- 2026Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception6 May 2026
- 2026Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment30 Mar 2026
- 2026AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception10 Feb 2026