This repository is supplementary material for the paper 'Nengo and low-powered AI hardware for robust, embedded control'. The code for running the examples is provided here, as well as the scripts for regenerating the data analysis in Figure 2.

These instructions assume you are using Anaconda to manage your Python environments, and have Git installed.

NOTE To run these examples on Loihi hardware you will need to adhere to the Loihi dependencies, including Python 3.5.2. Installation instructions can be found at: https://www.nengo.ai/nengo-loihi/installation.html. These examples can also be run using any other Nengo backend, including NengoLoihi's emulator. If you are targetting any backend aside from the Loihi hardware, please install the most recent Python.

To install the dependencies, run:

pip install -r requirements.txt

You will also need to install the latest versions of Nengo, NengoDL, NengoInterfaces, and NengoLoihi:

git clone https://github.com/nengo/nengo
cd nengo
pip install -e .
cd ..

git clone https://github.com/nengo/nengo-dl
cd nengo-dl
pip install -e .
cd ..

git clone https://github.com/nengo/nengo-loihi
cd nengo-loihi
pip install -e .
cd ..

git clone https://github.com/nengo/nengo-interfaces
cd nengo_interfaces
pip install -e .
cd ..

You can either run the rover using pre-trained vision weights, or regenerate the data to train your own instance of the vision network and use those weights.

python rover.py

Generate training and validation data

python rover.py generate_data

The script samples every 10th frame, and is set to generate 45k training images and 5k for validation. This may take some time depending on your machine.

Train up the network

python train_rover_vision.py

This will generate the weights file in the data folder. By default this will run for 50 epochs but convergence may occur before this.

Test the network to validate

python rover_vision.py data/weights

Run using the rover

python rover.py data/weights

You can see an example video of the adaptive controller running here.

To regenerate the figures from Figure 2, the neurorobotic adaptive controller example, you will first need to download the dataset. Once the dataset is downloaded to this folder, you can simply run:

python plot_full.py

To run the analysis and generate the graphs in Figure 2 of the paper.