  Our world is being increasingly pervaded by intelligent robots with varying
degrees of autonomy. To seamlessly integrate themselves in our society, these
machines should possess the ability to navigate the complexities of our daily
routines even in the absence of a human's direct input. In other words, we want
these robots to understand the intentions of their partners with the purpose of
predicting the best way to help them. In this paper, we present CASPER
(Cognitive Architecture for Social Perception and Engagement in Robots): a
symbolic cognitive architecture that uses qualitative spatial reasoning to
anticipate the pursued goal of another agent and to calculate the best
collaborative behavior. This is performed through an ensemble of parallel
processes that model a low-level action recognition and a high-level goal
understanding, both of which are formally verified. We have tested this
architecture in a simulated kitchen environment and the results we have
collected show that the robot is able to both recognize an ongoing goal and to
properly collaborate towards its achievement. This demonstrates a new use of
Qualitative Spatial Relations applied to the problem of intention reading in
the domain of human-robot interaction.
