Memory Robot Design: A New Perspective From Human Brain Model and Large Language Model

Abstract

With the spread of generative AI, the study proposed a memory-based cognitive robot architecture by using a Large Language Model (LLM), inspired by the working memory of the human brain model. A card-pairing task is designed to perform visual working memory with 60 participants with the measurement of electroencephalography (EEG). The proposed human brain model is a muti-featured EEG model, represented by the concept of brain energy, connectivity, and complexity. Band power ratios show that the anti-correlation of alpha and gamma waveforms can be observed in the occipital lobe. Brain connectivity is represented through magnitude squared coherence and phase locking value, and brain complexity is calculated by the Katz fractal dimension. Three pathways—attention, short-term memory, and distraction resistance are revealed. The changes in Katz fractal dimension are discovered in the frontal and occipital lobes. A novel memory architecture of robot cognition designed from human brain model includes three types of memory—short-term memory, working memory, and long-term memory, which is driven by GPT-4o. Two memory tests, a recalling test, and a working memory test, are conducted to validate the memory ability of the robot. The precision / recall / F1-score of working memory performance is 1.0 / 0.35 / 0.519, and the average accuracy of recalling test is 0.7. It is demonstrated that the different types of human memory functions can be implemented in the cognitive robot architecture driven by LLM. The research not only provides insight into the working memory of the human brain model but also realizes the robot architecture with the application of generative AI.