Affiliation:
1. Delft University of Technology
Abstract
Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior 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-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g 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 optimized behavior 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.
Subject
Artificial Intelligence,General Biochemistry, Genetics and Molecular Biology
Cited by
27 articles.
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1. Evolutionary Machine Learning in Robotics;Handbook of Evolutionary Machine Learning;2023-11-02
2. A Framework for Learning Behavior Trees in Collaborative Robotic Applications;2023 IEEE 19th International Conference on Automation Science and Engineering (CASE);2023-08-26
3. Interactively learning behavior trees from imperfect human demonstrations;Frontiers in Robotics and AI;2023-07-12
4. Creating Trustworthy AI for UAS using Labeled Backchained Behavior Trees;2023 International Conference on Unmanned Aircraft Systems (ICUAS);2023-06-06
5. Skill-based design of dependable robotic architectures;Robotics and Autonomous Systems;2023-02