A Robust Controller for Stable 3D Pinching using Tactile Sensing

TL;DR

This paper introduces a tactile-based control system enabling stable 3D pinching of unknown objects with robotic fingers, validated through simulation and real-world experiments, demonstrating robustness and potential for in-hand manipulation.

Contribution

The paper presents a novel tactile sensing-based controller for stable 3D grasping of unknown objects, integrating deep learning for contact surface orientation estimation.

Findings

Achieved stable equilibrium poses on various objects.

Demonstrated robustness to perturbations and measurement errors.

Validated system performance in both simulation and real-world experiments.

Abstract

This paper proposes a controller for stable grasping of unknown-shaped objects by two robotic fingers with tactile fingertips. The grasp is stabilised by rolling the fingertips on the contact surface and applying a desired grasping force to reach an equilibrium state. The validation is both in simulation and on a fully-actuated robot hand (the Shadow Modular Grasper) fitted with custom-built optical tactile sensors (based on the BRL TacTip). The controller requires the orientations of the contact surfaces, which are estimated by regressing a deep convolutional neural network over the tactile images. Overall, the grasp system is demonstrated to achieve stable equilibrium poses on various objects ranging in shape and softness, with the system being robust to perturbations and measurement errors. This approach also has promise to extend beyond grasping to stable in-hand object manipulation with multiple fingers.