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、直观且高效地创建机器人程序具有重要价值。此外，机器人需通过响应式策略进行控制，以应对其运行环境的不可预测性。本文提出了一种融合示范学习行为树与遗传编程进化机制的方法框架，专用于协作机器人应用场景。本文的主要贡献在于证明：通过结合这两种学习方法，可获得一种允许非专业用户以半自动、高效且交互的方式生成行为树的方法。我们通过一系列操作实验验证了该框架的有效性。行为树完全在仿真环境中完成学习，随后成功迁移至真实协作机器人平台。