  Affordance detection from visual input is a fundamental step in autonomous
robotic manipulation. Existing solutions to the problem of affordance detection
rely on convolutional neural networks. However, these networks do not consider
the spatial arrangement of the input data and miss parts-to-whole
relationships. Therefore, they fall short when confronted with novel,
previously unseen object instances or new viewpoints. One solution to overcome
such limitations can be to resort to capsule networks. In this paper, we
introduce the first affordance detection network based on dynamic
tree-structured capsules for sparse 3D point clouds. We show that our
capsule-based network outperforms current state-of-the-art models on viewpoint
invariance and parts-segmentation of new object instances through a novel
dataset we only used for evaluation and it is publicly available from
github.com/gipfelen/DTCG-Net. In the experimental evaluation we will show that
our algorithm is superior to current affordance detection methods when faced
with grasping previously unseen objects thanks to our Capsule Network enforcing
a parts-to-whole representation.
