EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation
This addresses the problem of robotic manipulation in hospital settings where transparent objects are common, offering a solution for tasks like grasping transfusion bags, but it is incremental as it builds on existing segmentation methods.
The paper tackles the challenge of vision-based perception for robots handling deformable and transparent objects in hospitals, presenting EfficientPPS for part-aware panoptic segmentation, which demonstrated robust and efficient grasping of transparent transfusion bags with a collaborative robot arm.
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.