The vernissage corpus: a conversational human-robot-interaction dataset

Authors:
Dinesh Babu Jayagopi;Samira Sheiki;David Klotz;Johannes Wienke;Jean-Marc Odobez;Sebastien Wrede;Vasil Khalidov;Laurent Nyugen;Britta Wrede;Daniel Gatica-Perez
Affiliations:
Idiap Research Institute, Martigny, Switzerland;Idiap Research Institute and PFL, Martigny, Switzerland;Bielefeld University, Bielefeld, Germany;Bielefeld University, Bielefeld, Germany;Idiap Research Institute and PFL, Martigny, Switzerland;Bielefeld University, Bielefeld, Germany;Idiap Research Institute, Martigny, Switzerland;Idiap Research Institute, Martigny, Switzerland;Bielefeld University, Bielefeld, Germany;Idiap Research Institute and PFL, Martigny, Switzerland
Venue:
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Year:
2013

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Abstract

We introduce a new conversational Human-Robot-Interaction (HRI) dataset with a real-behaving robot inducing interactive behavior with and between humans. Our scenario involves a humanoid robot NAO1 explaining paintings in a room and then quizzing the participants, who are naive users. As perceiving nonverbal cues, apart from the spoken words, plays a major role in social interactions and socially-interactive robots, we have extensively annotated the dataset. It has been recorded and annotated to benchmark many relevant perceptual tasks, towards enabling a robot to converse with multiple humans, such as speaker localization and speech segmentation; tracking, pose estimation, nodding, visual focus of attention estimation in visual domain; and an audio-visual task such as addressee detection. NAO system states are also available. As compared to recordings done with a static camera, this corpus involves the head-movement of a humanoid robot (due to gaze change, nodding), posing challenges to visual processing. Also, the significant background noise present in a real HRI setting makes auditory tasks challenging.