Simon Stepputtis

Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy. Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems. In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities inherent to multimodal inference in such scenarios. We also empirically show that our method results in more accurate, more robust, and faster inference than standard Interaction Primitives and other common methods in challenging HRI scenarios.

@InProceedings{campbell17a,
        title = {Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction},
        author = {Joseph Campbell and Heni Ben Amor},
        booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
        pages = {379–387},
        year = {2017},
        editor = {Sergey Levine and Vincent Vanhoucke and Ken Goldberg},
        volume = {78},
        series = {Proceedings of Machine Learning Research},
        address = {},
        month = {13–15 Nov},
        publisher = {PMLR},
        pdf = {http://proceedings.mlr.press/v78/campbell17a/campbell17a.pdf},
        url = {http://proceedings.mlr.press/v78/campbell17a.html}
}