Neural cellular automaton for developing body and brain of a modular robot
Code and videos for the paper: arXiv Preprint
Dependencies used:
- Python 3.8.10
- TensorFlow 2.6
- gym 0.21.0
- pygame 2.0.1
- box2d-py 2.3.5
- cma 3.1.0
- numpy 1.19.5
- moviepy 3.1.1
- imageio 2.9.0
Playing light chasing task:
python modular_light_chaser.py
Playing carrying ball to target:
python modular_carrier.py
Train light chasing task with CMA-ES:
python train_modular_light_chaser_es.py
Train light chasing with obstacle task with CMA-ES:
python train_modular_light_chaser_es.py --wall
Train carrying ball to target task with CMA-ES:
python train_modular_carrier_es.py
Display training options using python train_modular_light_chaser_es.py --help:
usage: train_modular_light_chaser_es.py [-h] [--maxgen MAXGEN] [--popsize POPSIZE] [--threads THREADS] [--width WIDTH] [--height HEIGHT] [--stdinit STDINIT] [--bodytype BODYTYPE] [--retrain RETRAIN] [--saveall] [--wall]
optional arguments:
-h, --help show this help message and exit
--maxgen MAXGEN Maximum number of generations
--popsize POPSIZE Population size
--threads THREADS Number of threads
--width WIDTH CA width
--height HEIGHT CA height
--stdinit STDINIT Standard deviation to initialize the Evolution Strategy
--bodytype BODYTYPE Premade body types: {1,2,3}
--retrain RETRAIN path to log directory for retraining
--saveall Save all solutions
--wall Wall environment
Test trained NCA:
python test_modular_*.py --logdir <path>
Generate test video of trained NCA:
python test_video_modular_*.py --logdir <path>
This folder contains sample videos of the trained models reported in the paper.
@inproceedings{pontes2022unified,
title={A Unified Substrate for Body-Brain Co-evolution},
author={Pontes-Filho, Sidney and Walker, Kathryn and Najarro, Elias and Nichele, Stefano and Risi, Sebastian},
booktitle={From Cells to Societies: Collective Learning across Scales},
year={2022}
}