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Our world is being increasingly pervaded by intelligent robots with varyingdegrees of autonomy. To seamlessly integrate themselves in our society, thesemachines should possess the ability to navigate the complexities of our dailyroutines even in the absence of a human's direct input. In other words, we wantthese robots to understand the intentions of their partners with the purpose ofpredicting the best way to help them. In this paper, we present CASPER(Cognitive Architecture for Social Perception and Engagement in Robots): asymbolic cognitive architecture that uses qualitative spatial reasoning toanticipate the pursued goal of another agent and to calculate the bestcollaborative behavior. This is performed through an ensemble of parallelprocesses that model a low-level action recognition and a high-level goalunderstanding, both of which are formally verified. We have tested thisarchitecture in a simulated kitchen environment and the results we havecollected show that the robot is able to both recognize an ongoing goal and toproperly collaborate towards its achievement. This demonstrates a new use ofQualitative Spatial Relations applied to the problem of intention reading inthe domain of human-robot interaction.

CASPER: Cognitive Architecture for Social Perception and Engagement in Robots