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

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arxiv 2503.09100 v1 pith:SQZGJMIQ submitted 2025-03-12 cs.RO cs.CV

classification cs.ROcs.CV
keywords tactilesensorsvision-basedimagestacchicomputationalcostmethod
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception

    cs.RO 2026-05 unverdicted novelty 6.0 of 10

    A neural model reduces high-resolution tactile elastomer simulation cost by over 65% while improving geometric fidelity and enabling differentiable inference.

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