@inproceedings{Soh:2012:10.1109/IROS.2012.6385992,
author = {Soh, H and Su, Y and Demiris, Y},
doi = {10.1109/IROS.2012.6385992},
pages = {4489--4496},
publisher = {IEEE},
title = {Online Spatio-Temporal Gaussian Process Experts with Application to Tactile Classification},
url = {http://dx.doi.org/10.1109/IROS.2012.6385992},
year = {2012}
}

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TY  - CPAPER
AB  - In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams. Our first contribution is a new recursive kernel, which we have integrated into a sparse Gaussian Process to yield the Spatio-Temporal Online Recursive Kernel Gaussian Process (STORK-GP). This algorithm iteratively learns from time-series, providing both predictions and uncertainty estimates. Experiments on benchmarks demonstrate that our method achieves high accuracies relative to state-of-the-art methods. Second, we contribute an online tactile classifier which uses an array of STORK-GP experts. In contrast to existing work, our classifier is capable of learning new objects as they are presented, improving itself over time. We show that our approach yields results comparable to highly-optimised offline classification methods. Moreover, we conducted experiments with human subjects in a similar online setting with true-label feedback and present the insights gained.
AU  - Soh,H
AU  - Su,Y
AU  - Demiris,Y
DO  - 10.1109/IROS.2012.6385992
EP  - 4496
PB  - IEEE
PY  - 2012///
SN  - 2153-0858
SP  - 4489
TI  - Online Spatio-Temporal Gaussian Process Experts with Application to Tactile Classification
UR  - http://dx.doi.org/10.1109/IROS.2012.6385992
UR  - http://hdl.handle.net/10044/1/12658
ER  - 

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