{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:58:09Z","timestamp":1776333489972,"version":"3.51.2"},"reference-count":26,"publisher":"MIT Press - Journals","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Artificial Life"],"published-print":{"date-parts":[[2016,2]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1162\/artl_a_00192","type":"journal-article","created":{"date-parts":[[2016,2,17]],"date-time":"2016-02-17T15:49:09Z","timestamp":1455724149000},"page":"23-48","source":"Crossref","is-referenced-by-count":42,"title":["Behavior Trees for Evolutionary Robotics"],"prefix":"10.1162","volume":"22","author":[{"given":"Kirk Y. 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