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Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter
We present joint learning of instance and semantic segmentation for visible
and occluded region masks. Sharing the feature extractor with instance
occlusion segmentation, we introduce semantic occlusion segmentation into the
instance segmentation model. This joint learning fuses the instance- and
image-level reasoning of the mask prediction on the different segmentation
tasks, which was missing in the previous work of learning instance segmentation
only (instance-only). In the experiments, we evaluated the proposed joint
learning comparing the instance-only learning on the test dataset. We also
applied the joint learning model to 2 different types of robotic pick-and-place
tasks (random and target picking) and evaluated its effectiveness to achieve
real-world robotic tasks.Comment: 7 pages, 13 figures, IEEE International Conference on Robotics and
Automation (ICRA) 201