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.

介绍了一种符号认知架构 CASPER，运用定性空间推理来实现智能机器人理解他人意图并协作完成任务的目的。通过模拟的厨房环境测试表明，机器人可以识别正在进行的目标并适当地协作完成任务。这是一个新型的定性空间关系应用于人机交互意图读取问题的方法。