An ACT-R Observer Model for Anticipatory Assistive Robots

Interactions between human users and assistive robotic systems in real life often involve both cognitive and physical interactions. In order to support humans well in their daily life, a robotic agent needs to be aware of the situation, anticipate the human agent, and generate human-like behaviors. In this work, we present an ACT-R observer model as a possible implementation on the robotic agent’s cognitive level. The model anticipates the human agent’s behaviors in an application example: a tea-making task. We discuss how such a model provides us the possibility to connect cognitive and physical human-robot interactions, and the advantages of such a model compared with common state-of-the-art approaches for human intention and behavior predictions. We also discuss how such an individual ACT-R model provides potential for an anticipatory, situation-aware robotic agent in real life applications, allowing us to solve ambiguities from acquiring input via various sensors and gain time for proactive support.