Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication

TL;DR

This survey reviews machine learning techniques for intelligent materials with embedded distributed computation, focusing on detection, classification, control, and the adaptation of models for robotic material systems.

Contribution

It compiles and compares machine learning approaches for embedded systems in materials, and explores their adaptation to amorphous networks for robotic applications.

Findings

Distributed SVMs, graphical models, and mixture models are reviewed.

Potential architectures for integrating ML into robotic nodes are discussed.

Deep learning for automatic feature extraction in materials is considered.

Abstract

This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements. The purpose of this survey is to identify desired tasks to be performed in each type of material or structure (e.g., damage detection in composites), identify and compare common approaches to learning such tasks, and investigate models and training paradigms used. Machine learning approaches and common temporal features used in the domains of structural health monitoring, morphable aircraft, wearable computing and robotic skins are explored. As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered. Distributed versions of support vector machines, graphical models and mixture models developed in the field of wireless sensor networks are reviewed. Potential areas of investigation, including possible architectures for incorporating machine learning into robotic nodes, training approaches, and the possibility of using deep learning approaches for automatic feature extraction, are discussed.