Learning Behavior Trees with Genetic Programming in Unpredictable Environments

• URL: http://arxiv.org/abs/2011.03252v1
• Date: Fri, 6 Nov 2020 09:28:23 GMT
• Title: Learning Behavior Trees with Genetic Programming in Unpredictable Environments
• Authors: Matteo Iovino, Jonathan Styrud, Pietro Falco and Christian Smith
• Abstract summary: We show that genetic programming can be effectively used to learn the structure of a behavior tree. We demonstrate that the learned BTs can solve the same task in a realistic simulator, reaching convergence without the need for task specifics.
• Score: 7.839247285151348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent changes to the task. In this paper, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. Moreover, we propose to use a simple simulator for the learning and demonstrate that the learned BTs can solve the same task in a realistic simulator, reaching convergence without the need for task specific heuristics. The learned solution is tolerant to faults, making our method appealing for real robotic applications.

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