Workshop on Cognitive Architectures for Situated Multimodal Human Robot Language Interaction

October 16th, in Boulder, Colorado

The workshop will take place in conjunction with the 20th ACM International Conference on Multimodal Interaction (ICMI 2018) in Boulder, Colorado on the 16h of October

In many application fields of human robot interaction, robots need to adapt to changing contexts and thus be able to learn tasks from non-expert humans through verbal and non-verbal interaction. Inspired by human cognition, we are interested in various aspects of learning, including multimodal representations, mechanisms for the acquisition of concepts (words, objects, actions), memory structures etc., up to full models of socially guided, situated, multimodal language interaction. These models can then be used to test theories of human situated multimodal interaction, as well as to inform computational models in this area of research.

Call for Papers

The workshop aims at bringing together linguists, computer scientists, cognitive scientists, and psychologists with a particular focus on embodied models of situated natural language interaction. Workshop submissions should answer at least one of the following questions:

  • Which kind of data is adequate to develop socially guided models of language acquisition, e.g. multimodal interaction data, audio, video, motion tracking, eye tracking, force data (individual or joint object manipulation)?
  • How should empirical data be collected and preprocessed in order to develop cognitively inspired models of language acquisition, e.g. should either HH or HR data be collected?
  • Which mechanisms are needed by the artificial system to deal with the multimodal complexity of human interaction? How can the information transmitted via different modalities be combined at a higher level of abstraction)?
  • Models of language learning through multimodal interaction: How should semantic representations or mechanisms for language acquisition look like to allow an extension through multi-modal interaction?
  • Based on the above representations, which machine learning approaches are best suited to handle the multimodal, time-varying and possibly high dimensional data? How can the system learn incrementally in an open-ended fashion?

Invited Speakers

Keynotes will be given by John Laird, Professor at the faculty of the Computer Science and Engineering Division of the Electrical Engineering and Computer Science Department of the University of Michigan, and Chen Yu, Professor at the Computational Cognition and Learning Lab at Indiana University.

Important Dates

  • Paper submission deadline: June 29, 2018
  • Notification of acceptance: July 20, 2018
  • Final version: August 3, 2018
  • Workshop: October 16, 2018

Submission Instructions

Articles should be 4-6 pages, formatted using the ACM template of the ICMI conference. For each accepted contribution, at least one of the authors is required to attend the workshop.


Stephanie Gross, Austrian Research Institute for Artificial Intelligence, Vienna, Austria
Brigitte Krenn, Austrian Research Institute for Artificial Intelligence, Vienna, Austria
Matthias Scheutz, Department of Computer Science at Tufts University, Massachusetts, USA
Matthias Hirschmanner, Automation and Control Institute at Vienna University of Technology, Vienna, Austria