ICRA 2011 Paper Abstract

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Paper TuP201.1

Song, Dan (Royal Inst. of Tech. (KTH), Stockholm), Ek, Carl Henrik (Royal Institute of Technology), Huebner, Kai (Royal Inst. of Tech. (KTH), Stockholm), Kragic, Danica (KTH)

Multivariate Discretization for Bayesian Network Structure Learning in Robot Grasping

Scheduled for presentation during the Regular Sessions "Human and Multi-Robot Interaction" (TuP201), Tuesday, May 10, 2011, 15:25−15:40, Room 3B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 29, 2020

Keywords Learning and Adaptive Systems, Cognitive Human-Robot Interaction, AI Reasoning Methods

Abstract

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.

 

 

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