# Embedded Neural Networks for Robot Autonomy

arXiv: https://arxiv.org/abs/1911.03848 · HTML: https://mlsanity.com/paper/1911.03848/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [High-bandwidth nonlinear control for soft actuators with recursive network models](https://mlsanity.com/paper/2101.01139/) (2021)
- [Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems](https://mlsanity.com/paper/2205.10940/) (2022)

## Abstract (arXiv)

We present a library to automatically embed signal processing and neural network predictions into the material robots are made of. Deep and shallow neural network models are first trained offline using state-of-the-art machine learning tools and then transferred onto general purpose microcontrollers that are co-located with a robot's sensors and actuators. We validate this approach using multiple examples: a smart robotic tire for terrain classification, a robotic finger sensor for load classification and a smart composite capable of regressing impact source localization. In each example, sensing and computation are embedded inside the material, creating artifacts that serve as stand-in replacement for otherwise inert conventional parts. The open source software library takes as inputs trained model files from higher level learning software, such as Tensorflow/Keras, and outputs code that is readable in a microcontroller that supports C. We compare the performance of this approach for various embedded platforms. In particular, we show that low-cost off-the-shelf microcontrollers can match the accuracy of a desktop computer, while being fast enough for real-time applications at different neural network configurations. We provide means to estimate the maximum number of parameters that the hardware will support based on the microcontroller's specifications.