Learning from demonstration is a promising way of teaching robots new skills. However, a central problem when executing acquired skills is to recognize risks and failures. This is essential since the demonstrations usually cover only a few mostly successful cases. Inevitable errors during execution require specific reactions that were not apparent in the demonstrations. In this paper, we focus on teaching the robot situational awareness from an initial skill demonstration via kinesthetic teaching and sparse labeling of autonomous skill executions as safe or risky. At runtime, our system, called ILeSiA, detects risks based on the perceived camera images by encoding the images into a low-dimensional latent space representation and training a classifier based on the encoding and the provided labels. In this way, ILeSiA boosts the confidence and safety with which robotic skills can be executed. Our experiments demonstrate that classifiers, trained with only a small amount of user-provided data, can successfully detect numerous risks. The system is flexible because the risk cases are defined by labeling data. This also means that labels can be added as soon as risks are identified by a human supervisor. We provide all code and data required to reproduce our experiments at imitrob.ciirc.cvut.cz/publications/ilesia.

翻译：从演示中学习是一种有前景的机器人技能教学方法。然而，执行已习得技能时的一个核心问题在于识别风险与故障。这一点至关重要，因为演示通常仅涵盖少数基本成功的案例。执行过程中不可避免的错误需要特定的应对反应，而这些反应在演示中并未显现。本文聚焦于通过以下方式向机器人传授情境感知能力：基于初始技能演示（通过动觉示教完成），并对自主技能执行过程进行稀疏标注，将其标记为安全或危险。在运行时，我们提出的ILeSiA系统通过将感知到的相机图像编码为低维潜在空间表示，并基于该编码与提供的标注训练分类器，从而检测风险。通过这种方式，ILeSiA显著提升了机器人技能执行的置信度与安全性。实验表明，仅需少量用户提供的数据进行训练，分类器即可成功检测多种风险。本系统具备灵活性，因为风险案例通过数据标注定义。这也意味着一旦人类监督者识别出风险，即可随时添加标注。我们在imitrob.ciirc.cvut.cz/publications/ilesia提供了重现实验所需的全部代码与数据。