We have permanently deployed Hala; the world first's English and Arabic Robot Receptionist for 500+ days in an uncontrolled multi-cultural/multi-lingual environment within Carnegie Mellon Qatar.

Hala serves as a research testbed for studying the influence of socio-cultural norms and the nature of interactions during human-robot interaction within a multicultural, yet primarily ethnic Arab, setting.

Hala, as a platform, owes its uptime to several independently engineered components for modeling user interactions, syntactic and semantic language parsing, inviting users with a laser, handling facial animations, text-to-speech and lip synchronization, error handling and reporting, post dialogue analysis, networking/interprocess communication, and a rich client interface.

We conjecture that disparities in discourse, appearances, and non-verbal gestures amongst interlocutors of different cultures and native tongues. By varying Hala's behavior and responses, we gain insight into these disparities (if any) and therefore we've calculated: rate of thanks after the robot's answer amongst cultures, user willingness to answer personal questions, correlation between language and acceptance of robot invites, the duration of conversations, effectiveness of running an open-ended experiment versus surveys.

We want to understand if people communicate with a robot (rather, an inanimate object with human-like characteristics) differently than amongst themselves. Additionally, we want to extrapolate these differences/similarities while accounting for culture and language. Our results indicate that users in Qatar thanked Hala less frequently than their counterparts in the US. The robot often answered personal questions and inquiries (for ex: her marital status, job satisfaction, etc); however, only 10% of the personal questions posed by the robot were answered by users. We observed a 34% increase in interactions when the robot initiated the conversation by inviting nearby users, and the subsequent duration of the conversation also increased by 30%. Upon bringing language into the mix, we observed that native Arabic speakers were twice more likely to accept an invite from the robot and they also tended to converse for 25% longer than other cultures.

These results indicate a disparity in interaction across English and Arabic users thereby encouraging the creation of culture specific dialogues, appearances and non-verbal gestures for an engaging social robot with regionally relevant applications.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error