Previous methods for Learning from Demonstration leverage several approaches for a human to teach motions to a robot, including teleoperation, kinesthetic teaching, and natural demonstrations. However, little previous work has explored more general interfaces that allow for multiple demonstration types. Given the varied preferences of human demonstrators and task characteristics, a flexible tool that enables multiple demonstration types could be crucial for broader robot skill training. In this work, we propose Versatile Demonstration Interface (VDI), an attachment for collaborative robots that simplifies the collection of three common types of demonstrations. Designed for flexible deployment in industrial settings, our tool requires no additional instrumentation of the environment. Our prototype interface captures human demonstrations through a combination of vision, force sensing, and state tracking (e.g., through the robot proprioception or AprilTag tracking). Through a user study where we deployed our prototype VDI at a local manufacturing innovation center with manufacturing experts, we demonstrated the efficacy of our prototype in representative industrial tasks. Interactions from our study exposed a range of industrial use cases for VDI, clear relationships between demonstration preferences and task criteria, and insights for future tool design.

翻译：以往的演示学习方法利用多种方式让人类向机器人传授动作，包括遥操作、示教编程和自然演示。然而，先前研究很少探索能够支持多种演示类型的通用接口。考虑到人类演示者的不同偏好和任务特性，一个支持多种演示类型的灵活工具可能对更广泛的机器人技能训练至关重要。在本工作中，我们提出多功能演示接口（VDI），这是一种协作机器人的附加装置，可简化三种常见演示类型的采集过程。该工具专为工业环境中的灵活部署而设计，无需对环境进行额外仪器配置。我们的原型接口通过视觉、力传感和状态跟踪（例如通过机器人本体感知或AprilTag跟踪）的组合来捕获人类演示。通过在本地制造创新中心与制造专家进行用户研究并部署原型VDI，我们在代表性工业任务中验证了原型的有效性。研究中的交互揭示了VDI的一系列工业应用场景、演示偏好与任务标准之间的明确关系，以及未来工具设计的启示。