HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning

TL;DR

HyperTaxel introduces a contrastive learning framework to enhance the spatial resolution of taxel-based tactile signals, capturing geometric contact features and improving downstream robotic manipulation tasks.

Contribution

The paper presents a novel geometrically-informed representation and contrastive learning approach for high-resolution tactile signal encoding from low-resolution sensors.

Findings

Outperforms baseline methods in tactile representation quality

Captures geometric features like flatness, curvature, and edges

Enhances downstream tasks such as surface classification and pose estimation

Abstract

To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-standardized representations. In this paper, we propose a novel framework, HyperTaxel, for learning a geometrically-informed representation of taxel-based tactile signals to address challenges associated with their spatial resolution. We use this representation and a contrastive learning objective to encode and map sparse low-resolution taxel signals to high-resolution contact surfaces. To address the uncertainty inherent in these signals, we leverage joint probability distributions across multiple simultaneous contacts to improve taxel hyper-resolution. We evaluate our representation by comparing it with two baselines and present results that suggest our representation outperforms the baselines. Furthermore, we present qualitative results that demonstrate the learned representation captures the geometric features of the contact surface, such as flatness, curvature, and edges, and generalizes across different objects and sensor configurations. Moreover, we present results that suggest our representation improves the performance of various downstream tasks, such as surface classification, 6D in-hand pose estimation, and sim-to-real transfer.