  Behaviour selection has been an active research topic for robotics, in
particular in the field of human-robot interaction. For a robot to interact
effectively and autonomously with humans, the coupling between techniques for
human activity recognition, based on sensing information, and robot behaviour
selection, based on decision-making mechanisms, is of paramount importance.
However, most approaches to date consist of deterministic associations between
the recognised activities and the robot behaviours, neglecting the uncertainty
inherent to sequential predictions in real-time applications. In this paper, we
address this gap by presenting a neurorobotics approach based on computational
models that resemble neurophysiological aspects of living beings. This
neurorobotics approach was compared to a non-bioinspired, heuristics-based
approach. To evaluate both approaches, a robot simulation is developed, in
which a mobile robot has to accomplish tasks according to the activity being
performed by the inhabitant of an intelligent home. The outcomes of each
approach were evaluated according to the number of correct outcomes provided by
the robot. Results revealed that the neurorobotics approach is advantageous,
especially considering the computational models based on more complex animals.
