PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Active sequential learning with tactile feedback
Hannes Saal, Jo-Anne Ting and Sethu Vijayakumar
International Conference on Artificial Intelligence and Statistics 2010.


We consider the problem of tactile discrimination, with the goal of estimating an underlying state parameter in a sequential setting. If the data is continuous and high dimensional, collecting enough representative data samples becomes difficult. We present a framework that uses active learning to help with the sequential gathering of data samples, using information-theoretic criteria to find optimal actions at each time step. We consider two approaches to recursively update the state parameter belief: an analytical Gaussian approximation and a Monte Carlo sampling method. We show how both active frameworks improve convergence, demonstrating results are on a real robotic hand-arm system that estimates the viscosity of liquids from tactile feedback data.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5992
Deposited By:Jo-Anne Ting
Deposited On:08 March 2010