Learning to generalize 3D spatial relationships

This paper presents an approach to learn meaningful spatial relationships in an unsupervised fashion from the distribution of 3D object poses in the real world. Our approach begins by extracting an over-complete set of features to describe the relative geometry of two objects. Each relationship type is modeled using a relevance-weighted distance over this feature space. This effectively ignores irrelevant feature dimensions. Our algorithm RANSEM for determining subsets of data that share a relationship as well as the model to describe each relationship is based on robust sample-based clustering. This approach combines the search for consistent groups of data with the extraction of models that precisely capture the geometry of those groups. An iterative refinement scheme has shown to be an effective approach for finding concepts of differing degrees of geometric specificity. Our results show that the models learned by our approach correlate strongly with the English labels that have been given by a human annotator to a set of validation data drawn from the NYUv2 real-world Kinect dataset, demonstrating that these concepts can be automatically acquired given sufficient experience. Additionally, the results of our method significantly out-perform K-means, a standard baseline for unsupervised cluster extraction.

Authors

• Jimmy Li 0001
• David Meger
• Gregory Dudek

Keywords

• Computational modeling
• Feature extraction
• Data models
• Three-dimensional displays
• Context
• Training data
• Robots
• Learning Models
• Types Of Relationships
• Feature Dimension
• Meaningful Relationships
• Left Side
• Unsupervised Learning
• Unsupervised Methods
• Concept Of Learning
• Mahalanobis Distance
• Ground Truth Labels
• Training Examples
• Robot Control
• Semantic Knowledge
• Continuous Features
• Objects In The Scene
• Discrete Features
• Object Pairs
• Dishwasher
• Inliers
• Objects In Context
• Relevant Weight
• Object Labels
• Dimensional Weight