TactileAR: Active Tactile Pattern Reconstruction

TL;DR

TactileAR introduces a method to reconstruct high-resolution contact surface shapes from low-resolution tactile sensor data using a Kalman filter framework and active exploration, improving robotic tactile sensing accuracy.

Contribution

The paper presents a novel framework combining a Gaussian degradation model and Kalman filtering for high-resolution tactile surface reconstruction from low-res data.

Findings

Effective reconstruction of complex contact surfaces demonstrated.

Active exploration improves reconstruction efficiency.

Outperforms prior approaches in real-world tests.

Abstract

High-resolution (HR) contact surface information is essential for robotic grasping and precise manipulation tasks. However, it remains a challenge for current taxel-based sensors to obtain HR tactile information. In this paper, we focus on utilizing low-resolution (LR) tactile sensors to reconstruct the localized, dense, and HR representation of contact surfaces. In particular, we build a Gaussian triaxial tactile sensor degradation model and propose a tactile pattern reconstruction framework based on the Kalman filter. This framework enables the reconstruction of 2-D HR contact surface shapes using collected LR tactile sequences. In addition, we present an active exploration strategy to enhance the reconstruction efficiency. We evaluate the proposed method in real-world scenarios with comparison to existing prior-information-based approaches. Experimental results confirm the efficiency of the proposed approach and demonstrate satisfactory reconstructions of complex contact surface shapes. Code: https://github.com/wmtlab/tactileAR