LLM-based robot personality simulation and cognitive system

摘要

The inherence of personality in human-robot interaction enhances conversational dynamics and user experience. The deployment of Chat GPT-4 within a cognitive robot framework is designed by using state-space realization to emulate specific personality traits, incorporating elements of emotion, motivation, visual attention, and both short-term and long-term memory. The encoding and retrieval of long-term memory are facilitated through document embedding techniques, while emotions are generated based on predictions of future events. This framework processes textual and visual information, responding or initiating actions in accordance with the configured personality settings and cognitive processes. The constancy and effectiveness of the personality simulation have been compared to human baseline and validated via two personality assessments: the International Personality Item Pool - Neuroticism, Extraversion and Openness (IPIP-NEO) and the Big Five personality test. Our proposed personality model of cognitive robot is designed by using Kelly's role construct repertory, Cattell's 16 personality factors and preferences, which are analyzed by construct validity and compared to human subjects. Theory of mind is observed in personality simulation, which perform better second-order of belief compared to other agent on the improved theory of mind dataset (ToMi dataset). Based on the proposed methods, our designed robot, Mobi, is enable to chat based on its own personality, handle social conflicts and understand user's intent. Such simulations can achieve a high degree of human likeness, characterized by conversations that are flexible and imbued with intention.