Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes
This addresses the need for trust and safety in human-robot interaction scenarios by providing interpretable models for robotic agents.
The paper tackled the problem of interpretable affordance detection on 3D point clouds by applying prototypical learning methods, achieving competitive performance with state-of-the-art black-box models on the 3D-AffordanceNet benchmark dataset.
Robotic agents need to understand how to interact with objects in their environment, both autonomously 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 prototypical models achieve competitive performance with state-of-the-art black-box models and offer inherent interpretability. This makes prototypical models a promising candidate for human-robot interaction scenarios that require increased trust and safety.