Active Sequential Learning with Tactile Feedback
Abstract
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 on a real robotic hand-arm system that estimates the viscosity of liquids from tactile feedback data.
Cite
Text
Saal et al. "Active Sequential Learning with Tactile Feedback." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Saal et al. "Active Sequential Learning with Tactile Feedback." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/saal2010aistats-active/)BibTeX
@inproceedings{saal2010aistats-active,
title = {{Active Sequential Learning with Tactile Feedback}},
author = {Saal, Hannes and Ting, Jo–Anne and Vijayakumar, Sethu},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {677-684},
volume = {9},
url = {https://mlanthology.org/aistats/2010/saal2010aistats-active/}
}