论文标题
从演示中学到的交互式人类的操纵技巧协调
Interactive Human-in-the-loop Coordination of Manipulation Skills Learned from Demonstration
论文作者
论文摘要
从演示中学习（LFD）为程序机器人技能提供了一个快速，直观和高效的框架，这对研究和工业应用都越来越兴趣。大多数复杂的操纵任务是长期的，并且涉及一组技能原则。因此，在各种情况下，拥有一个可靠的协调方案，该方案选择可靠的协调方案，该方案选择正确的技能原始序列和每个技能的正确参数。这项工作不是依靠精确的模拟器，而是为LFD技能提出了一个人类的协调框架，该框架是：从动力学演示中构建参数化技能模型；根据人类指示构建一个几何任务网络（GTN）；在执行过程中逐步学习层次控制策略。该框架可以大大减少手动设计工作，同时改善对新场景的适应性。我们在一个7-DOF的机器人操纵器上显示，所提出的方法可以在不到30分钟的时间内教授复杂的工业任务，例如垃圾箱分类和组装。
Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are long-term and involve a set of skill primitives. Thus it is crucial to have a reliable coordination scheme that selects the correct sequence of skill primitive and the correct parameters for each skill, under various scenarios. Instead of relying on a precise simulator, this work proposes a human-in-the-loop coordination framework for LfD skills that: builds parameterized skill models from kinesthetic demonstrations; constructs a geometric task network (GTN) on-the-fly from human instructions; learns a hierarchical control policy incrementally during execution. This framework can reduce significantly the manual design efforts, while improving the adaptability to new scenes. We show on a 7-DoF robotic manipulator that the proposed approach can teach complex industrial tasks such as bin sorting and assembly in less than 30 minutes.
