Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing

TL;DR

This paper introduces Tac-VGNN, a novel graph neural network leveraging Voronoi features for improved tactile pose estimation and servoing using optical tactile sensors, outperforming CNN-based methods in accuracy and control smoothness.

Contribution

The paper presents a new Voronoi graph neural network architecture tailored for tactile pose estimation, combining GNNs with Voronoi features to enhance interpretability and efficiency.

Findings

28.57% improvement in pose estimation accuracy along vertical depth

Enhanced data interpretability and training efficiency over CNN-based methods

Smoother robot control trajectories in surface following tasks

Abstract

Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to high-resolution optical tactile sensors. Given the recent successes of Graph Neural Network (GNN) and the effectiveness of Voronoi features, we developed a Tactile Voronoi Graph Neural Network (Tac-VGNN) to achieve reliable pose-based tactile servoing relying on a biomimetic optical tactile sensor (TacTip). The GNN is well suited to modeling the distribution relationship between shear motions of the tactile markers, while the Voronoi diagram supplements this with area-based tactile features related to contact depth. The experiment results showed that the Tac-VGNN model can help enhance data interpretability during graph generation and model training efficiency significantly than CNN-based methods. It also improved pose estimation accuracy along vertical depth by 28.57% over vanilla GNN without Voronoi features and achieved better performance on the real surface following tasks with smoother robot control trajectories. For more project details, please view our website: https://sites.google.com/view/tac-vgnn/home