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The images captured by vision-based tactile sensors carry information abouthigh-resolution tactile fields, such as the distribution of the contact forcesapplied to their soft sensing surface. However, extracting the informationencoded in the images is challenging and often addressed with learning-basedapproaches, which generally require a large amount of training data. Thisarticle proposes a strategy to generate tactile images in simulation for avision-based tactile sensor based on an internal camera that tracks the motionof spherical particles within a soft material. The deformation of the materialis simulated in a finite element environment under a diverse set of contactconditions, and spherical particles are projected to a simulated image.Features extracted from the images are mapped to the 3D contact forcedistribution, with the ground truth also obtained via finite-elementsimulations, with an artificial neural network that is therefore entirelytrained on synthetic data avoiding the need for real-world data collection. Theresulting model exhibits high accuracy when evaluated on real-world tactileimages, is transferable across multiple tactile sensors without furthertraining, and is suitable for efficient real-time inference.

Sim-to-real for high-resolution optical tactile sensing: From images to 3D contact force distributions