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

arXiv: https://arxiv.org/abs/1606.03508 · HTML: https://mlsanity.com/paper/1606.03508/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Controlling diverse robots by inferring Jacobian fields with deep networks](https://mlsanity.com/paper/2407.08722/) (2024)
- [Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks](https://mlsanity.com/paper/2302.05517/) (2023)
- [Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems](https://mlsanity.com/paper/2205.10940/) (2022)

## Abstract (arXiv)

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.