Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights

TL;DR

This paper demonstrates that combining morphological evolution and learning in hexacopter drones leads to significantly improved performance on complex tasks and provides new analytical insights into their interaction.

Contribution

It introduces a novel approach to evolve and learn in aerial robots, showing performance gains and new metrics for analyzing morphology and controller interactions.

Findings

Evolved drones outperform traditional hexacopters on complex tasks.

New metrics reveal previously unknown effects of morphology and learning interaction.

Analysis tools are domain-agnostic, aiding future embodied AI research.

Abstract

Evolution and learning have historically been interrelated topics, and their interplay is attracting increased interest lately. The emerging new factor in this trend is morphological evolution, the evolution of physical forms within embodied AI systems such as robots. In this study, we investigate a system of hexacopter-type drones with evolvable morphologies and learnable controllers and make contributions to two fields. For aerial robotics, we demonstrate that the combination of evolution and learning can deliver non-conventional drones that significantly outperform the traditional hexacopter on several tasks that are more complex than previously considered in the literature. For the field of Evolutionary Computing, we introduce novel metrics and perform new analyses into the interaction of morphological evolution and learning, uncovering hitherto unidentified effects. Our analysis tools are domain-agnostic, making a methodological contribution towards building solid foundations for embodied AI systems that integrate evolution and learning.