EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects
  for Robotic Manipulation
Benjamin Alt Minh Dang Nguyen Andreas Hermann Darko Katic Rainer Jäkel Rüdiger Dillmann Eric Sax
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.