Behaviour Trees for Evolutionary Robotics

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 rate of the optimised behaviour in simulation is 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.