Retrieving Memory Content from a Cognitive Architecture by Impressions from Language Models for Use in a Social Robot

摘要

Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of memory. We investigate a way to create a social robot with a human-like memory and recollection based on cognitive processes for a better comprehensible and situational behavior of the robot. Using a combined system consisting of an Adaptive Control of Thought-Rational (ACT-R) model and a humanoid social robot, we show how recollections from the declarative memory of the ACT-R model can be retrieved using data obtained by the robot via an LLM or VLM, processed according to the procedural memory of the cognitive model and returned to the robot as instructions for action. Real-world data captured by the robot can be stored as memory chunks in the cognitive model and recalled, for example by means of associations. This opens up possibilities for using human-like judgment and decision-making capabilities inherent in cognitive architectures with social robots and practically offers opportunities of augmenting the prompt for LLM-driven utterances with content from declarative memory, thus keeping them more contextually relevant. We illustrate the use of such an approach in HRI scenarios with the social robot Pepper.