论文标题
在无法预测的环境中学习具有遗传编程的行为树
Learning Behavior Trees with Genetic Programming in Unpredictable Environments
论文作者
论文摘要
现代工业应用要求机器人能够在不可预测的环境中运行，并且可以用最少的努力创建程序，因为任务可能经常发生更改。在本文中，我们表明，遗传编程可有效地用于学习行为树（BT）的结构，以在无法预测的环境中解决机器人任务。此外，我们建议使用一个简单的模拟器进行学习，并证明学习的BT可以在现实的模拟器中解决相同的任务，而无需特定任务的启发式方法即可达到融合。博学的解决方案宽容，使我们的方法吸引了真正的机器人应用。
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.
