Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks

Joseph Campbell, Simon Stepputtis, Heni Ben Amor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XV
EditorsAntonio Bicchi, Hadas Kress-Gazit, Seth Hutchinson
PublisherMIT Press Journals
ISBN (Print)9780992374754
StatePublished - 2019
Event15th Robotics: Science and Systems, RSS 2019 - Freiburg im Breisgau, Germany
Duration: Jun 22 2019Jun 26 2019

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Conference15th Robotics: Science and Systems, RSS 2019
CityFreiburg im Breisgau

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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