This is the official repository for the GECCO (Genetic and Evolutionary Computation Conference, 2022) paper

Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention
Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha

hosting all the code necessary to replicate the experiments. This work was carried out at the Evolutionary Robotics and Artificial Life Laboratory (ERALLab) at the University of Trieste (Italy). More videos available at this link.

By running

java -cp libs:JGEA.jar:libs/TwoDimHighlyModularSoftRobots.jar:target/AttentionVSRs.jar world.units.erallab.Main {args}

where {args} is a placeholder for the arguments you provide (see below), you will launch an evolutionary optimization for evolving jointly the controller (a self attention-based artificial neural network) of Voxel-based Soft Robots (VSRs). At the same time, evolution metadata will be saved inside the output folder. The project has been tested with Java 14.0.2.

• src contains all the source code for the project;
• libs contains the .jar files for the dependencies (see below);
• Data_Analysis_Notebook.ipynb is a Jupyter Notebook (Python) with some routines and starter code to perform analysis on the evolution output files.

It relies on:

• JGEA, for the evolutionary optimization;
• 2D-VSR-Sim, for the simulation of VSRs.

The relative jars have already been included in the directory libs. See pom.xml for more details on dependencies.

This is a table of possible command-line arguments:

where {...} denotes a finite and discrete set of possible choices for the corresponding argument. The description for each argument is as follows:

• exp: the controller architecture to experiment with, either an MLP baseline or self attention-based.
• config: none-homo for MLP baseline without communication, none-neumann for fixed-communication MLP baseline, and none-4-8-8-homo|homo-tanh for self-attention without communication.
• shape: the VSR shape to experiment with.
• seed: the random seed for the experiment.
• threads: the number of threads to perform evolution with. Defaults to the number of available cores on the current CPU. Parallelization is taken care by JGEA and implements a distributed fitness assessment.

Please cite as:

@article{pigozzi2022evolving,
  title={Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention},
  author={Pigozzi, Federico and Tang, Yujin and Medvet, Eric and Ha, David},
  journal={arXiv preprint arXiv:2204.06481},
  year={2022}
}