Assistive robotic manipulators are becoming increasingly important for people with disabilities. Teleoperating the manipulator in mundane tasks is part of their daily lives. Instead of steering the robot through all actions, applying self-recorded motion macros could greatly facilitate repetitive tasks. Dynamic Movement Primitives (DMP) are a powerful method for skill learning via teleoperation. For this use case, however, they need simple heuristics to specify where to start, stop, and parameterize a skill without a background in computer science and academic sensor setups for autonomous perception. To achieve this goal, this paper provides the concept of local, global, and hybrid skills that form a modular basis for composing single-handed tasks of daily living. These skills are specified implicitly and can easily be programmed by users themselves, requiring only their basic robotic manipulator. The paper contributes all details for robot-agnostic implementations. Experiments validate the developed methods for exemplary tasks, such as scratching an itchy spot, sorting objects on a desk, and feeding a piggy bank with coins. The paper is accompanied by an open-source implementation at https://github.com/fzi-forschungszentrum-informatik/ArNe

翻译：辅助机器人操作臂对于残障人士正变得日益重要。在日常任务中通过遥操作控制机械臂是他们日常生活的一部分。相较于全程操控机器人完成所有动作，应用自记录的动作宏能极大简化重复性任务。动态运动基元（DMP）是通过遥操作进行技能学习的强大方法。然而，在此应用场景中，它们需要简单的启发式规则来指定技能的起始点、终止点及参数化，而无需用户具备计算机科学背景或部署用于自主感知的学术级传感器装置。为实现这一目标，本文提出了局部技能、全局技能与混合技能的概念，这些技能构成了单手日常生活任务的模块化基础。这些技能通过隐式方式定义，用户仅需使用其基础机器人操作臂即可轻松自行编程。本文提供了与机器人无关的实现细节。实验验证了所开发方法在示例任务中的有效性，例如抓挠痒处、整理桌面物品及往存钱罐投币。本文附带开源实现代码，详见https://github.com/fzi-forschungszentrum-informatik/ArNe。