Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping

TL;DR

This paper presents a learning-based method using the PapillArray tactile sensor to detect incipient slip in robotic gripping, achieving high accuracy and robustness across different environments.

Contribution

The study introduces a novel learning-based approach with data augmentation for incipient slip detection using the PapillArray sensor, demonstrating high success rates and transferability.

Findings

95.6% detection success rate on offline data

96.8% success rate in robotic gripping environments

Robust performance with data augmentation methods

Abstract

The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-based approach to detect incipient slip using the PapillArray (Contactile, Australia) tactile sensor. The resulting model is highly effective in identifying patterns associated with incipient slip, achieving a detection success rate of 95.6% when tested with an offline dataset. Furthermore, we introduce several data augmentation methods to enhance the robustness of our model. When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96.8%, providing timely feedback for stabilizing several practical gripping tasks. Our project website: https://sites.google.com/view/incipient-slip-detection.