The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

When performing physical collaboration tasks, like packing a picnic basket together, humans communicate strongly and often subtly via multiple channels like gaze, speech, gestures, movement and posture. Understanding and participating in this communication enables us to predict a physical action rather than react to it, producing seamless collaboration. In this paper, we automatically learn key discriminative features that predict the intent to handover an object using machine learning techniques. We train and test our algorithm on multi-channel vision and pose data collected from an extensive user study in an instrumented kitchen. Our algorithm outputs a tree of possibilities, automatically encoding various types of pre-handover communication. A surprising outcome is that mutual gaze and inter-personal distance, often cited as being key for interaction, were not key discriminative features. Finally, we discuss the immediate and future impact of this work for human-robot interaction.

Notes

Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Personal Robotics

Text Reference

Kyle Strabala, Min Kyung Lee, Anca Dragan, Jodi Forlizzi, and Siddhartha Srinivasa, "Learning the Communication of Intent Prior to Physical Collaboration," 21st IEEE International Symposium on Robot and Human Interactive Communication, September, 2012.

BibTeX Reference

@inproceedings{Strabala_2012_7258,
   author = "Kyle Strabala and Min Kyung Lee and Anca Dragan and Jodi Forlizzi and Siddhartha Srinivasa",
   title = "Learning the Communication of Intent Prior to Physical Collaboration",
   booktitle = "21st IEEE International Symposium on Robot and Human Interactive Communication",
   month = "September",
   year = "2012",
}

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