EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation

The use of autonomous robots for assistance tasks in hospitals has the potential to free up qualified staff and improve patient care. However, the ubiquity of deformable and transparent objects in hospital settings poses significant 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 manipulation 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.