An RGB-D based social behavior interpretation system for a humanoid social robot

Zaraki, A., Giuliani, M., Dehkordi, M. B., Mazzei, D., D'ursi, A. and Rossi, D. D. (2014) An RGB-D based social behavior interpretation system for a humanoid social robot. In: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, 15-17 October 2014. Tehran, Iran: IEEE, pp. 157-168 Available from: http://eprints.uwe.ac.uk/31046

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Publisher's URL: http://dx.doi.org/10.1109/ICRoM.2014.6990898

Abstract/Description

We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human–robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Best Paper Award, Best Presentation Award
Uncontrolled Keywords:active vision, context-dependent social gaze behavior, human-robot interaction, scene analysis, social attention
Faculty/Department:Faculty of Environment and Technology > Department of Engineering Design and Mathematics
ID Code:31046
Deposited By: Dr M. Giuliani
Deposited On:21 Feb 2017 16:00
Last Modified:21 Feb 2017 16:00

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