Recently, programing by demonstration (PbD) received much attention for its capacity of fast programming with increasing demands in the robot manipulation area, especially in industrial applications. However, one of the biggest challenges of PbD is the recognition of demonstrator's finger high-fidelity motions especially in the environments with uncertainties, which limits the efficiency and accuracy of PbD. In this article, inspired by human dexterity, a novel PbD approach using the implicit muscular task planning strategy is presented to extract features from the arms’ giant movement and the hands’ fine motions during the demonstrator's operation. Furthermore, we integrate a deep reinforcement learning control method that further improves the manipulations’ adaptive ability in the unknown or dynamic environments. The experimental results show that our proposed approach can deal with relative complex assembly tasks with a success rate of more than 67% within a fit tolerance of 4.2 mm by one-shot demonstration.

中文翻译：

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示范性的编程使人类肌肉任务计划与机器人的鲁棒性和精巧的操纵相结合

最近，演示编程（PbD）因其快速编程的能力而受到了广泛关注，在机器人操纵领域，尤其是在工业应用中，这种编程的需求不断增长。但是，PbD的最大挑战之一是如何识别演示者的手指高保真运动，尤其是在不确定的环境中，这限制了PbD的效率和准确性。在本文中，受人的灵巧性启发，提出了一种使用隐式肌肉任务计划策略的新颖PbD方法，以从演示者操作期间的手臂巨型运动和手部精细动作中提取特征。此外，我们集成了深度强化学习控制方法，可进一步提高操作在未知或动态环境中的自适应能力。