How can I help you? Comparing engagement classification strategies for a robot bartender

Foster, Mary Ellen; Gaschler, Andre; Giuliani, Manuel

Abstract

A robot agent existing in the physical world must be able to understand the social states of the human users it interacts with in order to respond appropriately. We compared two implemented methods for estimating the engagement state of customers for a robot bartender based on low-level sensor data: a rule-based version derived from the analysis of human behaviour in real bars, and a trained version using supervised learning on a labelled multimodal corpus. We first compared the two implementations using cross-validation on real sensor data and found that nearly all classifier types significantly outperformed the rule-based classifier. We also carried out feature selection to see which sensor features were the most informative for the classification task, and found that the position of the head and hands were relevant, but that the torso orientation was not. Finally, we performed a user study comparing the ability of the two classifiers to detect the intended user engagement of actual customers of the robot bartender; this study found that the trained classifier was faster at detecting initial intended user engagement, but that the rule-based classifier was more stable.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 15th International Conference on Multimodal Interfaces (ICMI 2013)
• Start Date: Dec 9, 2013
• End Date: Dec 13, 2013
• Acceptance Date: Dec 3, 2013
• Publication Date: Dec 3, 2013
• Peer Reviewed: Peer Reviewed
• Keywords: social signal processing, supervised learning
• Public URL: https://uwe-repository.worktribe.com/output/925137
• Publisher URL: http://dl.acm.org/citation.cfm?id=2522879
• Additional Information: Title of Conference or Conference Proceedings : 15th ACM on International Conference on Multimodal Interaction