Semi-Supervised Disentanglement of Tactile Contact~Geometry from Sliding-Induced Shear

TL;DR

This paper introduces a semi-supervised method to remove shear distortion from tactile images, enabling accurate shape reconstruction and pose estimation with less supervision, improving robotic tactile sensing.

Contribution

A novel semi-supervised approach effectively disentangles shear effects from tactile contact images, matching supervised performance with significantly less labeled data.

Findings

Accurately reconstructs contact geometry masked by shear.

Achieves comparable results to fully supervised methods.

Requires an order of magnitude less supervision.

Abstract

The sense of touch is fundamental to human dexterity. When mimicked in robotic touch, particularly by use of soft optical tactile sensors, it suffers from distortion due to motion-dependent shear. This complicates tactile tasks like shape reconstruction and exploration that require information about contact geometry. In this work, we pursue a semi-supervised approach to remove shear while preserving contact-only information. We validate our approach by showing a match between the model-generated unsheared images with their counterparts from vertically tapping onto the object. The model-generated unsheared images give faithful reconstruction of contact-geometry otherwise masked by shear, along with robust estimation of object pose then used for sliding exploration and full reconstruction of several planar shapes. We show that our semi-supervised approach achieves comparable performance to its fully supervised counterpart across all validation tasks with an order of magnitude less supervision. The semi-supervised method is thus more computational and labeled sample-efficient. We expect it will have broad applicability to wide range of complex tactile exploration and manipulation tasks performed via a shear-sensitive sense of touch.