Behaviour Trees for Evolutionary Robotics
Kirk Y.W. Scheper, Sjoerd Tijmons, Coen C. de Visser, Guido C.H.E. de, Croon

TL;DR
This paper demonstrates the first use of Behaviour Trees in Evolutionary Robotics on a real drone, improving behavior interpretability and enabling autonomous flight through real-world testing.
Contribution
It introduces Behaviour Trees to Evolutionary Robotics, enhancing behavior transparency and manual adaptability in real robotic platforms.
Findings
Behavior Trees improve behavior interpretability.
The drone achieved 54% success rate in real-world tests.
Performance surpasses traditional user-defined controllers.
Abstract
Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing Micro Air Vehicle equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success…
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Taxonomy
TopicsReinforcement Learning in Robotics · Distributed Control Multi-Agent Systems · Biomimetic flight and propulsion mechanisms
