用虚拟现实让人类示范机器人动作，安全又高效。
Extended Reality System for Robotic Learning from Human Demonstration
- 通过扩展现实技术实现人机动作示范，避免物理风险。
- 在真实任务中达到与实体机器人示范相当的学习效果。
- 适合需要安全示范的复杂操作场景，如厨房烹饪。
许多现实任务对人类来说直观易行，但难以算法化编码以供机器人执行。在此类场景中，机器人系统可通过专家示范学习任务执行方式。然而，在某些情境下（如使用刀具切菜），使用物理机器人进行示范可能存在困难或安全隐患。扩展现实技术为示范机器人轨迹提供了自然且安全的环境，并支持更丰富的交互方式。本文提出一种通用的扩展现实示范系统——RADER（Robot Action Demonstration in Extended Reality），并将其应用于现有最先进的从示范学习方法中，实验表明：通过该系统提供的示范与物理机器人示范相比，取得了相近的学习效果。
原文摘要 · Abstract (English)
Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each task. In many settings, it may be difficult or unsafe to use a physical robot to provide these demonstrations, for example, considering cooking tasks such as slicing with a knife. Extended reality provides a natural setting for demonstrating robotic trajectories while bypassing safety concerns and providing a broader range of interaction modalities. We propose the Robot Action Demonstration in Extended Reality (RADER) system, a generic extended reality interface for learning from demonstration. We additionally present its application to an existing state-of-the-art learning from demonstration approach and show comparable results between demonstrations given on a physical robot and those given using our extended reality system.
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