|dc.contributor.author||Duque Garcia, Ismael||
|dc.description.abstract||A challenge in Human-Robot Interaction is tailoring the social skills of robot
companions to match those expected by individual humans during their rst encounter.
Currently, large amounts of user data are needed to con gure robot companions
with these skills. This creates the need of running long-term Human-Robot
Interaction studies in domestic environments. A new approach using personas is
explored to alleviate this arduous data collection task without compromising the
level of interaction currently shown by robot companions.
The personas technique was created by Alan Cooper in 1999 as a tool to de ne
user archetypes of a system in order to reduce the involvement of real users during
the development process of a target system. This technique has proven bene cial
in Human-Computer Interaction for years. Therefore, similar bene ts could be
expected when applying personas to Human-Robot Interaction. Our novel approach
de nes personas as the key component of a computational behaviour model used
to adapt robot companions to individual user's needs. This approach reduces the
amount of user data that must be collected before a Human-Robot Interaction study,
by associating new users to pre-de ned personas that adapt the robot behaviours
through their integration with the computational behaviour model. At the same
time that the current robot social interaction level expected by humans during the
rst encounter is preserved.
The University of Hertfordshire Robot House provided the naturalistic domestic
environment for the investigation. After incorporating a new module, an Activity
Recognition System, to increase the overall context-awareness of the system, a computational
behaviour model will be de ned through an iterative research process.
The initial de nition of the model was evolved after each experiment based on the
ndings. Two successive studies investigated personas and determined the steps to
follow for their integration into the targeted model. The nal model presented was
de ned from users' preferences and needs when interacting with a robot companion
during activities of daily living at home. The main challenge was identifying the
variables that match users to personas in our model. This approach open a new
discussion in the Human-Robot Interaction eld to de ne tools that help reduce the
amount of user data requiring collection prior to the rst interaction with a robot
companion in a domestic environment.
We conclude that modelling people's preferences when interacting with robot
companions is a challenging approach. Integrating the Human-Computer Interaction
technique into a computational behaviour model for Human-Robot Interaction
studies was more di cult than anticipated. This investigation shows the advantages
and disadvantages of introducing this technique into Human-Robot Interaction, and
explores the challenges in de ning a personas-based computational behaviour model.
The continuous learning process experienced helps clarify the steps that other researchers
in the eld should follow when investigating a similar approach. Some
interesting outcomes and trends were also found among users' data, which encourage
the belief that the personas technique can be further developed to tackle some
of the current di culties highlighted in the Human-Robot Interaction literature.||en_US
|dc.subject||Computational Behaviour Model||en_US
|dc.subject||User Activity Recognition||en_US
|dc.title||Adapting Robot Behaviour in Smart Homes: a Different Approach Using Personas||en_US