EvoRobogami: Co-designing with Humans in Evolutionary Robotics Experiments
Huang Zonghao, Quinn Wu, David Howard, Cynthia Sung

TL;DR
This paper investigates how human-designed robot configurations influence evolutionary algorithms in robotics, finding that combining human intuition with random solutions enhances performance, especially in intuitive problem spaces.
Contribution
It introduces a method for injecting human-generated designs into evolutionary robotics and assesses their impact on optimization outcomes.
Findings
Balanced initial populations improve results
Human designs benefit intuitive problems
Combining human and random solutions yields best performance
Abstract
We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed…
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