TacIPC: Intersection- and Inversion-free FEM-based Elastomer Simulation For Optical Tactile Sensors
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Abstract (arXiv)
Tactile perception stands as a critical sensory modality for human interaction with the environment. Among various tactile sensor techniques, optical sensor-based approaches have gained traction, notably for producing high-resolution tactile images. This work explores gel elastomer deformation simulation through a physics-based approach. While previous works in this direction usually adopt the explicit material point method (MPM), which has certain limitations in force simulation and rendering, we adopt the finite element method (FEM) and address the challenges in penetration and mesh distortion with incremental potential contact (IPC) method. As a result, we present a simulator named TacIPC, which can ensure numerically stable simulations while accommodating direct rendering and friction modeling. To evaluate TacIPC, we conduct three tasks: pseudo-image quality assessment, deformed geometry estimation, and marker displacement prediction. These tasks show its superior efficacy in reducing the sim-to-real gap. Our method can also seamlessly integrate with existing simulators. More experiments and videos can be found in the supplementary materials and on the website: https://sites.google.com/view/tac-ipc.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 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
- 2025TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Modelcited by 4
- 2026Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deploymentcited by 1
- 2026Tac-DINO: Learning Vision-Tactile Features with Patch Alignmentcited by 1
- 2026Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perceptioncited by 0
- 2026Tac-DINO: Learning Vision-Tactile Features with Patch Alignment10 Jun 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
- 2025TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model12 Sep 2025
- 2025Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation17 Apr 2025
- 2024Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing10 Jul 2024