Complex manipulation tasks can contain various execution branches of primitive skills in sequence or in parallel under different scenarios. Manual specifications of such branching conditions and associated skill parameters are not only error-prone due to corner cases but also quickly untraceable given a large number of objects and skills. On the other hand, learning from demonstration has increasingly shown to be an intuitive and effective way to program such skills for industrial robots. Parameterized skill representations allow generalization over new scenarios, which however makes the planning process much slower thus unsuitable for online applications. In this work, we propose a hierarchical and compositional planning framework that learns a Geometric Task Network (GTN) from exhaustive planners, without any manual inputs. A GTN is a goal-dependent task graph that encapsulates both the transition relations among skill representations and the geometric constraints underlying these transitions. This framework has shown to improve dramatically the offline learning efficiency, the online performance and the transparency of decision process, by leveraging the task-parameterized models. We demonstrate the approach on a 7-DoF robot arm both in simulation and on hardware solving various manipulation tasks.
翻译:复杂的操作任务可以按顺序或在不同情景下平行地包含原始技能的各种执行分支。这些分支条件和相关技能参数的手工规格不仅因转角案例而容易出错,而且由于大量物体和技能而迅速无法找到。另一方面,从示范中学习日益证明是一种直观和有效的方法来为工业机器人规划这种技能。参数化的技能表现使得能够对新情景进行概括化,但使规划过程大大放慢,因而不适于在线应用。在这项工作中,我们提出了一个等级和构成规划框架,从详尽的规划者那里学习几何任务网络(GTN),而没有任何手工投入。GTN是一个目标性任务图,它既概括了技能代表之间的过渡关系,又概括了这些转变背后的几何限制。这个框架表明,通过利用任务性参数化模型,大大改进了离线学习效率、在线表现和决策过程的透明度。我们在模拟和硬件解决各种操纵任务时,都展示了7-DoF机器人手臂的方法。