In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.

翻译：在现代工业协作机器人应用中，自动、直观和高效地创建机器人程序是非常有必要的。此外，为了应对不可预测的环境，机器人需要由反应策略来控制。在本文中，我们提出了一个框架，将从演示中学习行为树的方法与遗传规划发展行为树的方法相结合，用于协作机器人应用。本文的主要贡献在于展示了通过组合两种学习方法可以半自动地，高效地和交互性地生成行为树的方法，使非专业用户能够使用。我们用一系列操作实验验证了该框架。行为树在模拟中完全被学习，然后转移到了真实的协作机器人上。