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.

本研究解决了机器人技能执行中识别风险和失败的挑战，因为现有演示通常只包含少数成功案例。提出的ILeSiA系统通过运动教学和稀疏标记自主技能执行的安全性与风险，帮助机器人从演示中学习情境意识，从而提高技能执行的安全性。实验结果表明，仅使用少量用户提供的数据即可有效识别多种风险情况。

ILeSiA：通过摄像输入进行情境意识的互动学习