The use of autonomous robots for assistance tasks in hospitals has the
potential to free up qualified staff and im-prove patient care. However, the
ubiquity of deformable and transparent objects in hospital settings poses
signif-icant challenges to vision-based perception systems. We present
EfficientPPS, a neural architecture for part-aware panoptic segmentation that
provides robots with semantically rich visual information for grasping and
ma-nipulation tasks. We also present an unsupervised data collection and
labelling method to reduce the need for human involvement in the training
process. EfficientPPS is evaluated on a dataset containing real-world hospital
objects and demonstrated to be robust and efficient in grasping transparent
transfusion bags with a collaborative robot arm.

自主机器人在医院的辅助任务中的应用有可能解放合格员工，提高病人护理质量。然而，在医院环境中存在大量变形和透明物体，这对基于视觉感知系统带来重大挑战。我们提出了 EfficientPPS，一种用于部分感知全景分割的神经结构，可为机器人提供语义丰富的视觉信息以进行抓取和操纵任务。我们还提出了一种无监督数据收集和标记方法，以减少人工参与训练过程的需求。EfficientPPS 在包含真实医院物品的数据集上进行评估，并在与协作机械臂抓取透明输液袋方面表现出了强大和高效。