Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction
Abstract
This paper introduces a fully Bayesian reformulation of Interaction Primitives for human-robot interaction and collaboration. A key insight is that a subset of human-robot interaction is conceptually related to simultaneous localization and mapping techniques. Leveraging this insight we can significantly increase the accuracy of temporal estimation and inferred trajectories while simultaneously reducing the associated computational complexity. We show that this enables more complex human-robot interaction scenarios involving more degrees of freedom.
Cite
Text
Campbell and Ben Amor. "Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction." Proceedings of the 1st Annual Conference on Robot Learning, 2017.Markdown
[Campbell and Ben Amor. "Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction." Proceedings of the 1st Annual Conference on Robot Learning, 2017.](https://mlanthology.org/corl/2017/campbell2017corl-bayesian/)BibTeX
@inproceedings{campbell2017corl-bayesian,
title = {{Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction}},
author = {Campbell, Joseph and Ben Amor, Heni},
booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
year = {2017},
pages = {379-387},
volume = {78},
url = {https://mlanthology.org/corl/2017/campbell2017corl-bayesian/}
}