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  Mr Jared de Bruin  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents


  • Mary Ellen Foster: School of Computing Science
  • Jane Stuart Smith: School of Critical Studies

The increasing availability of socially-intelligent robots with functionality for a range of purposes, from guidance in museums (Gehle et al 2015), to companionship for the elderly (Hebesberger et al 2016), has motivated a growing number of studies attempting to evaluate and enhance Human-Robot Interaction (HRI). But, as Honig and Oron-Gilad (2018)’s review of recent work on understanding and resolving failures in HRI observes, most research has focussed on technical ways of improving robot reliability. They argue that progress requires a ‘holistic approach’ in which ‘[t]he technical knowledge of hardware and software must be integrated with cognitive aspects of information processing, psychological knowledge of interaction dynamics, and domain-specific knowledge of the user, the robot, the target application, and the environment’ (p.16). Honig and Oron-Gilad point to a particular need to improve the ecological validity of evaluating user communication in HRI, by moving away from experimental, single-person environments, with low-relevance tasks, mainly with younger adult users, to more natural settings, with users of different social profiles and communication strategies, where the outcome of successful HRI matters. This project will combine current advances in the development of real-world social robots with methods and insights from sociolinguistic theory. Specifically, it will make use of the MuMMER robot system, which is a humanoid robot designed to interact naturally and autonomously in public spaces (Foster et al., 2016; Foster et al., 2019). MuMMER has been originally designed to entertain and engage visitors to a shopping mall, thereby enhancing their overall experience in the mall. For a robot to be successful in this context, it must support human-robot interaction which is socially acceptable, helpful and entertaining for multiple, diverse users in a real-world context. The sociolinguistic context for enhancing human-robot interaction in a real-world setting will be Scotland’s largest city, Glasgow, home to a substantial socially and ethnically diverse population, with its own range of distinctive dialect and accents, from broad Glaswegian vernacular to educated Scottish Standard English (e.g. Stuart-Smith 1999; Macaulay 2005), as well as ‘Glaswasian’, spoken by Glasgow’s South Asian heritage communities (e.g. Lambert et al 2007). Glasgow is also one of the most researched dialect areas in the English-speaking world, and so provides a wealth of comparative sociolinguistic material as the basis for the project. The work on the PhD project will draw on sociolinguistically-informed observational studies of the MuMMER robot deployed in various locations across Glasgow, interacting with users from a range of social, ethnic, and language backgrounds. Based on the findings of these studies, the student will identify necessary technical modifications to the robot’s interaction strategy to respond to and address issues identified when the robot is interacting with a diverse set of users. The modified robot will be deployed in a new set of observational studies; if time permits, this process will be repeated with different deployment locations and different sets of users to ensure that as many Glaswegians as possible are ultimately able to interact comfortably with the robot, whatever their background.


1. Clift, R. (2016). Conversation Analysis. Cambridge: Cambridge University Press.
2. Coupland, N., Sarangi, S., & Candlin, C. N. (2014). Sociolinguistics and social theory. Routledge.
3. Foster M.E., Alami, R., Gestranius, O., Lemon, O., Niemela, M., Odobez, J-M., Pandey, A.M. (2016) The MuMMER Project: Engaging Human-Robot Interaction in Real-World Public Spaces. In: Agah A., Cabibihan J., Howard A., Salichs M., He H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science, vol 9979. Springer, Cham
4. Foster, M.E. et al. (2019). MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces. Proceedings of the AAAI Fall Symposium of Artificial Intelligence for Human-Robot Interaction (AI-HRI 2019).
5. Gehle R., Pitsch K., Dankert T., Wrede S. (2015). Trouble-based group dynamics in real-world HRI – Reactions on unexpected next moves of a museum guide robot., in 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2015 (Kobe), 407–412.
6. Hebesberger, D., Dondrup, C., Koertner, T., Gisinger, C., Pripfl, J. (2016). Lessons learned from the deployment of a long-term autonomous robot as companion in physical therapy for older adults with dementia: A mixed methods study. In: The Eleventh ACM/IEEE International Conference on Human Robot Interaction, 27–34.
7. Honig, S., & Oron-Gilad, T. (2018). Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development. Frontiers in Psychology, 9, 861.
8. Lambert, K., Alam, F., & Stuart-Smith, J. (2007). Investigating British Asian accents: studies from Glasgow. In J. Trouvain & W. Barry (Eds.), 16th International Congress of Phonetic Sciences (Issue August, pp. 1509–1512). Universität des Saarlandes.
9. Macaulay, Ronald K. S. and Oxford University Press. 2005. Talk that Counts: Age, Gender, and Social Class Differences in Discourse. Oxford: Oxford University Press.
10. Stuart-Smith, J. (1999). Glasgow: Accent and voice quality. In P. Foulkes & G. J. Docherty (Eds.), Urban voices: Accent Studies in the British Isles (pp. 203–222). Arnold.
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