A Hippocampus-Inspired Environment-Specific Knowledge Acquisition System Utilizing Common Knowledge with Contextual Information

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Osamu Nomura, Katsumi Tateno, Takashi Morie

Abstract: Home service robots acquire environment-specific knowledge through experiences (episodes) in a home to perform tasks autonomously. We propose a hippocampus-inspired memory system comprising an environment-specific knowledge module that handles episodic memory and a common knowledge module. The robot works in a home and needs to learn the locations of objects in the home with minimal user help to deliver or store objects. We employ a large language model (LLM) that runs on an edge device as the common knowledge module to protect the privacy of users. The environment-specific knowledge module acquires episodes and generates contextual information. The LLM receives contextual information generated by the environment-specific knowledge module, with which it infers an appropriate possible location. We verified that the LLM could infer the possible object locations and that the proposed system could minimize the required user effort.