A hybrid elastic-hyperelastic approach for simulating soft tactile sensors.

ABSTRACT: Efficient robotic grasping increasingly relies on artificial intelligence (AI) and tactile sensing technologies, which necessitate the acquisition of substantial data-a task that can often prove challenging. Consequently, the alternative of generating tactile data through precise and efficient simulations is becoming increasingly appealing. A significant challenge for simulating tactile sensors is balancing the trade-off between accuracy and processing time in simulation algorithms and models. To address this, we propose a hybrid approach that combines elastic and hyperelastic finite element simulations, complemented by convolutional neural networks (CNNs), to generate synthetic tactile maps of a soft capacitive tactile sensor. By leveraging a dataset of 53,400 real-world tactile maps, thi

SUBMITTER: De la Cruz Sanchez BA 

PROVIDER: S-EPMC12321859 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature