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Task Learning Using Graphical Programming and Human Demonstrations

机译:使用图形编程和人类示威活动的任务学习

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The next generation of robots will have to learn new tasks or refine the existing ones through direct interaction with the environment or through a teaching/coaching process in Programming by Demonstration (PbD) and Learning by Instruction frameworks. In this paper, we propose to extend the classical PbD approach with a graphical language that makes robot coaching easier. The main idea is based on graphical programming where the user designs complex robot tasks by using a set of low-level action primitives. Different to other systems, our action primitives are made general and flexible so that the user can train them online and therefore easily design high level tasks.
机译:下一代机器人必须通过与环境的直接互动或通过演示(PBD)和教学框架学习的教学/教练流程来学习新任务或通过教学/教练流程来改进现有任务或优化现有任务。在本文中,我们建议使用一种使机器人辅导更容易的图形语言来扩展经典的PBD方法。主要思想基于图形编程,用户通过使用一组低级动作基元设计复杂的机器人任务。与其他系统不同,我们的动作原语是一般的,灵活性,使用户可以在线训练,因此易于设计高级任务。

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