Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution

TL;DR

This paper introduces a novel approach where robot morphologies develop over evolutionary timescales, leading to improved gait diversity and exploration, by reversing the natural temporal hierarchy of evolution and development.

Contribution

It proposes a method to invert the typical evolution-development hierarchy by implementing morphological development across generations, enhancing gait quality and exploration in robotic evolution.

Findings

Developmental evolution produces better gaits than traditional methods.

The approach prevents premature convergence in evolutionary searches.

Validated on 2D and 3D robotic morphologies from literature.

Abstract

Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy -- evolution overarching development -- is observed for most multicellular lifeforms. When designing robots however, this tenet lifts: it becomes -- however natural -- a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots' morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.