Robotics: Science and Systems XV

Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks

Joseph Campbell, Simon Stepputtis, Heni Ben Amor


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.



    AUTHOR    = {Joseph Campbell AND Simon Stepputtis AND Heni Ben Amor}, 
    TITLE     = {Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2019}, 
    ADDRESS   = {FreiburgimBreisgau, Germany}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2019.XV.047}