Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.
Scheper et. al @cite_30 applied evolutionary learning to BTs for a real-world robotic (Micro Air Vehicle) application. It appears as the first real-world robotic application of evolving BTs. They used a (sub-optimal) manually crafted BT as an initial BT in the evolutionary learning process, and conducted experiments with a flying robot, while the BT that controls the robot is learning itself in every experiment. Finally, they demonstrated significant improvement in the performances of the evolved final BT comparing to the initial user-defined BT. While we take inspirations from this work, the downside is that this work require an initial BT for it to work, which goes against our model-free objective.
@cite_30
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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.