Calinon, S. (2016)
Stochastic learning and control in multiple coordinate systems
Intl Workshop on Human-Friendly Robotics (HFR).
Abstract
A probabilistic interpretation of model predictive control is presented, enabling extensions to multiple coordinate systems. The resulting controller follows a minimal intervention principle, by learning and retrieving invariant motion patterns through the coordination of several frames of reference. When combined with a generative model, the approach can be used in various human-robot applications that are discussed in the paper.
Bibtex reference
@inproceedings{Calinon16HFR,
author="Calinon, S.",
title="Stochastic learning and control in multiple coordinate systems",
booktitle="Intl Workshop on Human-Friendly Robotics",
year="2016",
pages="1--5",
address="Genova, Italy"
}