Using nn4mc

Train Model in PC

Save the model as an HDF5 file. In the future we will support ONNX. Please be mindful of the current limitations.

Compile using nn4mc

This can be done through our command-line tool, python code or on this website!

Flash Generated Code!

Go to your preferred IDE and integrate the nn4mc code into your project!

Cite us!

Manzano, S. A., Hughes, D., Simpson, C., Patel, R., & Correll, N. (2019). Embedded Neural Networks for Robot Autonomy. International Symposium of Robotics Research (ISRR 2019, Hanoi). arXiv preprint arXiv:1911.03848.

Open Source Products and Case Studies

The following are the products and projects in which nn4mc has been used and built!

nn4mc_cpp

C++ command line tool

nn4mc_py

Python library

nn4mc_web

Web-based compiler

Terrain Sensitive Tire

Project

High-bandwidth Control

Project

Online Prediction

Project

Publications

Here are the list of publications that involve nn4mc, if you'd like to cite the project you can cite the first one!

ISRR 2019, Hanoi

Embedded Neural Networks for Robot Autonomy

Manzano, S. A., Hughes, D., Simpson, C., Patel, R., & Correll, N. (ISRR 2019). Embedded Neural Networks for Robot Autonomy. arXiv preprint arXiv:1911.03848.
IJRR 2019

Materials that make Robots Smart

Hughes, D., Heckman, C., & Correll, N. (2019). Materials that make robots smart. The International Journal of Robotics Research, 38(12-13), 1338-1351.
ISER 2020, Malta

High-bandwidth Nonlinear Control

Manzano S.A., Xu P., Ly K., Shepherd R., Correll N. (2021) High-Bandwidth Nonlinear Control for Soft Actuators with Recursive Network Models. In: Siciliano B., Laschi C., Khatib O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_52
TBD

Incremental Learning

The future of nn4mc is to be a toolkit that allows the user to also perform incremental learning.
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