Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors

TL;DR

This paper introduces a learning-based slip detection method using durable, low-cost barometric tactile sensors, achieving over 91% accuracy and demonstrating robustness and generalization in real-world robotic manipulation tasks.

Contribution

The paper presents the first successful application of barometric tactile sensors combined with machine learning for slip detection in robotics.

Findings

Achieved slip detection accuracy >91%.

Robust to slip speed and direction.

Successfully generalized to real-world tasks.

Abstract

Despite the utility of tactile information, tactile sensors have yet to be widely deployed in industrial robotics settings. Part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. Although these sensors have a low resolution, they have many other desirable properties including high reliability and durability, a very slim profile, and a low cost. We are able to achieve slip detection accuracies of greater than 91% while being robust to the speed and direction of the slip motion. Further, we test our detector on two robot manipulation tasks involving common household objects and demonstrate successful generalization to real-world scenarios not seen during training. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for complex manipulation tasks such as slip compensation.