Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights
For aerial robotics and evolutionary computing, it demonstrates performance gains and provides analytical tools for embodied AI systems integrating evolution and learning.
This paper shows that combining evolution and learning produces unconventional hexacopter drones that significantly outperform traditional designs on complex tasks, and introduces domain-agnostic metrics revealing new insights into the interaction between morphological evolution and learning.
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.