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You Never get a Second Chance to Make a First Impression – Establishing how best to align human expectations about a robot’s performance based on the robot’s appearance and behaviour

   UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents

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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
  • Emily Cross: School of Psychology

Aims and objectives:

A major aim of social robotics is to create embodied agents that humans can instantly and automatically understand and interact with, using the same mechanisms that they use when interacting with each other. While considerable research attention has been invested in this endeavour, it is still the case that when humans encounter robots, they need time to understand how the robot works; in other words, people need time to learn to read the signals the robot generates. People consistently have expectations that are far too high for the artificial agents they encounter, which often leads to confusion and disappointment.

If we can better understand human expectations about robot capabilities based on the robot’s appearance (and/or initial behaviours) and ensure that those are aligned with the actual robot abilities, this should accelerate progress in human-robot interaction, specifically in the domains of human acceptance of robots in social settings and cooperative task performance between humans and robots. This project will combine expertise in robotic design and the social neuroscience of how we perceive and interact with artificial agents to develop a socially interactive robot designed for use in public spaces that requires (little or) no learning or effort for humans to interact with while carrying out tasks such as guidance, cooperative navigation, and interactive problem-solving tasks.

Proposed methods:

Computing Science: System development and integration (Developing operational models of interactive behaviour and implementing them on robot platforms); deployment of robot systems in lab-based settings and in real-world public spaces

Psychology/Brain Science: Behavioural tasks (questionnaires and measures of social perception, such as the Social Stroop task), non-invasive mobile brain imaging (functional near infrared spectroscopy) to record human brain activity when encountering the artificial agent in question.

Likely outputs:

empirically-based principles for social robot design to optimize alignment between robot’s appearance, user expectations, and robot performance, based on brain and behavioural data

A publicly available, implemented, and validated robot system embodying these principles

Empirical research papers detailing findings for a computing science audience (e.g., ACM Transactions on Human-Robot Interaction) a psychology/neuroscience audience (e.g., Psychological Science, Cognition) and a general audience, that draws on the multidisciplinary aspects of the work (PNAS, Current Biology), as well as papers at appropriate conferences and workshops such as Human-Robot Interaction, Intelligent Virtual Agents, CHI, and similar.


[Fos17] Foster, M. E.; Gaschler, A.; and Giuliani, M. Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction. International Journal of Social Robotics. July 2017. [Fos16] Foster, M. E.; Alami, R.; Gestranius, O.; Lemon, O.; Niemelä, M.; Odobez, J.; and Pandey, A. K. The MuMMER project: Engaging human-robot interaction in real-world public spaces. In Proceedings of the Eighth International Conference on Social Robotics, 2016.
[Cro19] Cross, E. S., Riddoch, K. A., Pratts, J., Titone, S., Chaudhury, B. & Hortensius, R. (2019). A neurocognitive investigation of the impact of socialising with a robot on empathy for pain. Philosophical Transactions of the Royal Society B.
[Hor18] Hortensius, R. & Cross, E.S. (2018). From automata to animate beings: The scope and limits of attributing socialness to artificial agents. Annals of the New York Academy of Science: The Year in Cognitive Neuroscience.
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