Machine Learning Enabled Force Sensing of a Smart Skin for Robotics

TL;DR

This paper presents a machine learning approach to artificial skin for robots that predicts load positions using strain data, enabling flexible, cost-effective tactile sensing on various surfaces.

Contribution

The study introduces a novel ML-based method for load position prediction on artificial skin, including single and multiple load scenarios, and deformable surfaces, reducing manufacturing complexity.

Findings

Accurate load position predictions achieved with ML models.

Effective multi-objective prediction for multiple loads.

Successful adaptation to deformable surfaces for tactile sensing.

Abstract

Artificial skin with the sense of touch can support robots to interact with the surrounding environment efficiently and accomplish complex tasks. Traditional multi-layered artificial skins require complex manufacturing processes which can result in high cost as well as limitations on the material and size of the skin. In this paper, we demonstrate a machine learning based approach to predict positions of point loads using the most direct response as input signal: strain distribution. Starting with the simplest problem, predicting the position of a single point load acting on a flat surface, an ML model is developed, trained, and tested. Accurate predictions are obtained from the ML model, parameters that affect the accuracy are discussed, and validation tests are performed. After that, the ML model is modified to solve multi-objective prediction problems: predicting positions and magnitudes of multiple point loads. In the end, the ML model is upgraded to a 2-step model to predict the position of a point load acting on a deformable surface. The demonstrated approach enables a normal untreated surface to feel a touch no matter what the surface is made of or how large or small the size of the surface is. Therefore, we believe this ML-based load position prediction approach could be a promising tool for applications such as flexible touch screens, smart skin for robots, and micro touch sensors.