CASPER: Cognitive Architecture for Social Perception and Engagement in Robots
This addresses the challenge of seamless human-robot interaction by improving robots' social perception, though it appears incremental as it applies existing qualitative spatial reasoning to intention reading in a specific domain.
The paper tackles the problem of enabling robots to understand and predict human intentions for effective collaboration, presenting CASPER, a symbolic cognitive architecture that uses qualitative spatial reasoning to anticipate goals and calculate collaborative behaviors, with testing in a simulated kitchen environment showing the robot can recognize goals and collaborate properly.
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.