This is the official repository for the Artificial Life journal (special issue on Emodied Intelligence) paper "Evolving Modularity in Soft Robots through an Embodied and Self-Organizing Neural Controller", 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 Department of Engineering and Architecture, University of Trieste (Italy).
By running
java -cp libs:JGEA.jar:libs/TwoDimHighlyModularSoftRobots.jar:target/SelfOrganizingController.jar Main {args}
where {args} is a placeholder for the arguments you must provide (see below), you will launch an evolutionary optimization of the embodied, self-organizing controller for voxel-based soft robots described in the paper. At the same time, a number of evolution metadata will be saved inside the output folder. The project has been tested with Java 14.0.2.
srccontains all the source code for the project;libscontains the .jar files for the dependencies (see below);targetcontains the main .jar file;pythoncontains python scripts for pretty-plotting the robots' evolved controllers.
The project relies on:
- JGEA, for the evolutionary optimization;
- 2D-VSR-Sim, for the simulation of voxel-based soft robots.
The corresponding 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:
| Argument | Type | Optional (yes/no) | Default |
|---|---|---|---|
| seed | integer | no | - |
| morphology | string | no | - |
| births | integer | no | - |
| pop | integer | no | - |
| configuration | {modular, plain, unmodular} | yes | plain |
| connectivity | {minimal, full} | yes | full |
| transfer | {true, false} | yes | false |
| threads | integer | yes | # available cores on CPU |
where {...} denotes a finite and discrete set of possible choices for the corresponding argument. The description for each argument is as follows:
- seed: the random seed for the experiment;
- morphology: the robot shape to evolve with;
- births: total number of births of the evolutionary algorithm;
- pop: population size of the evolutionary algorithm;
- configuration: one of the three modularity configurations investigated in the paper;
- connectivity: wether to evolve without topology optimization (minimal) or with (full), defaults to full;
- transfer: whether to perform re-optimization (as described in the paper) or not;
- 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.
TODO, if accepted