Learning Behavior Trees with Genetic Programming in Unpredictable Environments
ABB Corporate Research（ABB集团研究院）；ABB Robotics（ABB机器人）；Royal Institute of Technology (KTH)（瑞典皇家理工学院（KTH））
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
