Grasp, Slide, Roll: Comparative Analysis of Contact Modes for Tactile-Based Shape Reconstruction

TL;DR

This paper compares different tactile contact modes for robotic shape reconstruction, demonstrating that finger-grazing and palm-rolling improve efficiency and accuracy, reducing interactions by 34% and increasing accuracy by 55%.

Contribution

It introduces a comparative analysis of contact modes combined with an information-theoretic exploration framework for tactile shape reconstruction.

Findings

Finger-grazing and palm-rolling improve reconstruction speed.

Reduced physical interactions by 34%.

Enhanced accuracy by 55%.

Abstract

Tactile sensing allows robots to gather detailed geometric information about objects through physical interaction, complementing vision-based approaches. However, efficiently acquiring useful tactile data remains challenging due to the time-consuming nature of physical contact and the need to strategically choose contact locations that maximize information gain while minimizing physical interactions. This paper studies how different contact modes affect object shape reconstruction using a tactile-enabled dexterous gripper. We compare three contact interaction modes: grasp-releasing, sliding induced by finger-grazing, and palm-rolling. These contact modes are combined with an information-theoretic exploration framework that guides subsequent sampling locations using a shape completion model. Our results show that the improved tactile sensing efficiency of finger-grazing and palm-rolling translates into faster convergence in shape reconstruction, requiring 34% fewer physical interactions while improving reconstruction accuracy by 55%. We validate our approach using a UR5e robot arm equipped with an Inspire-Robots Dexterous Hand, showing robust performance across primitive object geometries.