Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes

Robotic agents need to understand how to interact with objects in their environment, both au-
tonomously and during human-robot interactions. Affordance detection on 3D point clouds, which
identifies object regions that allow specific interactions, has traditionally relied on deep learning
models like PointNet++, DGCNN, or PointTransformerV3. However, these models operate as black
boxes, offering no insight into their decision-making processes. Prototypical Learning methods,
such as ProtoPNet, provide an interpretable alternative to black-box models by employing a “this
looks like that” case-based reasoning approach. However, they have been primarily applied to
image-based tasks. In this work, we apply prototypical learning to models for affordance detection
on 3D point clouds. Experiments on the 3D-AffordanceNet benchmark dataset show that proto-
typical models achieve competitive performance with state-of-the-art black-box models and offer
inherent interpretability. This makes prototypical models a