Multivariate discretization for Bayesian Network structure learning in robot grasping

A major challenge in modeling with BNs is learning the structure from both discrete and multivariate continuous data. A common approach in such situations is to discretize continuous data before structure learning. However efficient methods to discretize high-dimensional variables are largely lacking. This paper presents a novel method specifically aiming at discretization of high-dimensional, high-correlated data. The method consists of two integrated steps: non-linear dimensionality reduction using sparse Gaussian process latent variable models, and discretization by application of a mixture model. The model is fully probabilistic and capable to facilitate structure learning from discretized data, while at the same time retain the continuous representation. We evaluate the effectiveness of the method in the domain of robot grasping. Compared with traditional discretization schemes, our model excels both in task classification and prediction of hand grasp configurations. Further, being a fully probabilistic model it handles uncertainty in the data and can easily be integrated into other frameworks in a principled manner.

Authors

• Dan Song 0002
• Carl Henrik Ek
• Kai Huebner
• Danica Kragic

Keywords

• Data models
• Feature extraction
• Robot sensing systems
• Principal component analysis
• Grasping
• Humans
• Bayesian Model
• Structure Learning
• Bayesian Network Structure
• Robotic Grasping
• Continuous Data
• Dimensionality Reduction
• Classification Task
• Mixture Model
• Discrete Data
• Gaussian Process
• Discretization Scheme
• Locally Linear Embedding
• Domain Method
• Training Data
• Ellipsoid
• Object Features
• Latent Space
• Discrete Variables
• Classification Rate
• Additive Gaussian Noise
• Directed Acyclic Graph
• Compact Representation
• Efficient Representation
• Gaussian Mixture Model
• Manipulation Tasks
• Covariance Function
• Task Constraints
• Discrete Method
• Data Reconstruction
• Observation Space