TactiVerse: Generalizing Multi-Point Tactile Sensing in Soft Robotics Using Single-Point Data

TL;DR

TactiVerse introduces a U-Net-based framework for soft tactile sensing that generalizes from single-point to multi-point contact detection, improving accuracy and scalability in soft robotics.

Contribution

The paper presents a novel deep learning architecture that accurately predicts contact geometry from limited data and enhances multi-point sensing through dataset augmentation.

Findings

Achieves a mean absolute error of 0.0589 mm in single-point sensing.

Significantly improves multi-point discrimination accuracy from 1.214 mm to 0.383 mm.

Demonstrates effective extrapolation of complex contact geometries from basic interactions.

Abstract

Real-time prediction of deformation in highly compliant soft materials remains a significant challenge in soft robotics. While vision-based soft tactile sensors can track internal marker displacements, learning-based models for 3D contact estimation heavily depend on their training datasets, inherently limiting their ability to generalize to complex scenarios such as multi-point sensing. To address this limitation, we introduce TactiVerse, a U-Net-based framework that formulates contact geometry estimation as a spatial heatmap prediction task. Even when trained exclusively on a limited dataset of single-point indentations, our architecture achieves highly accurate single-point sensing, yielding a superior mean absolute error of 0.0589 mm compared to the 0.0612 mm of a conventional regression-based CNN baseline. Furthermore, we demonstrate that augmenting the training dataset with multi-point contact data substantially enhances the sensor's multi-point sensing capabilities, significantly improving the overall mean MAE for two-point discrimination from 1.214 mm to 0.383 mm. By successfully extrapolating complex contact geometries from fundamental interactions, this methodology unlocks advanced multi-point and large-area shape sensing. Ultimately, it significantly streamlines the development of marker-based soft sensors, offering a highly scalable solution for real-world tactile perception.