Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors
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Abstract (arXiv)
Simulation is widely applied in robotics research to save time and resources. There have been several works to simulate optical tactile sensors that leverage either a smoothing method or Finite Element Method (FEM). However, elastomer deformation physics is not considered in the former method, whereas the latter requires a massive amount of computational resources like a computer cluster. In this work, we propose a pluggable and low computational cost simulator using the Taichi programming language for simulating optical tactile sensors, named as Tacchi . It reconstructs elastomer deformation using particles, which allows deformed elastomer surfaces to be rendered into tactile images and reveals contact information without suffering from high computational costs. Tacchi facilitates creating realistic tactile images in simulation, e.g., ones that capture wear-and-tear defects on object surfaces. In addition, the proposed Tacchi can be integrated with robotics simulators for a robot system simulation. Experiment results showed that Tacchi can produce images with better similarity to real images and achieved higher Sim2Real accuracy compared to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo with only the requirement of 1G memory space in GPU compared to a computer cluster applied for FEM. With Tacchi, physical robot simulation with optical tactile sensors becomes possible. All the materials in this paper are available at https://github.com/zixichen007115/Tacchi .
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jan 2023.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 4.
8 papers.
- 2024AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touchcited by 21
- 2024Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensingcited by 15
- 2025Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulationcited by 8
- 2026Data Pyramid for Embodied Manipulation: A Surveycited by 6
- 2025Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensorscited by 4
- 2026FlowTouch: View-Invariant Visuo-Tactile Predictioncited by 1
- 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
- 2026Data Pyramid for Embodied Manipulation: A Survey27 Jul 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
- 2026FlowTouch: View-Invariant Visuo-Tactile Prediction9 Mar 2026
- 2025Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation17 Apr 2025
- 2025Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors12 Mar 2025
- 2024Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing10 Jul 2024
- 2024AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch12 May 2024