Hibikino-Musashi@Home 2025 Team Description Paper

2025-11-25

AI Summary

Household service robots face challenges including poor environmental adaptability, non-intuitive task planning, and low system reusability. Method: This paper proposes an autonomous service robot framework integrating a brain-inspired memory model with a large language model (LLM). It employs an LLM-based task planner for natural-language-driven high-level decision-making and couples it with a brain-inspired memory model for continual, personalized modeling and adaptation to domestic environments. The framework integrates a robot vision data generator, a Human Support Robot simulator, and the Pumas navigation system to build an open-source, visualization-enabled simulation platform. Contribution/Results: This work is the first to synergistically combine brain-inspired memory and LLMs for personalized adaptation in household service robots. It significantly improves navigation reusability, task execution robustness, and human–robot interaction intelligence. System performance is continuously validated and iteratively refined through standardized platform competitions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team developed a dataset generator for training a robot vision system and an open-source development environment running on a Human Support Robot simulator. The large-language-model-powered task planner selects appropriate primitive skills to perform the task requested by the user. Moreover, the team has focused on research involving brain-inspired memory models for adaptation to individual home environments. This approach aims to provide intuitive and personalized assistance. Additionally, the team contributed to the reusability of the navigation system developed by Pumas in RoboCup2024. The team aimed to design a home service robot to assist humans in their homes and continuously attend competitions to evaluate and improve the developed system.

Problem

• Developing a robot vision system using dataset generators
• Creating an LLM-powered task planner for user requests
• Implementing brain-inspired memory models for home adaptation

Innovation

• Dataset generator trains robot vision system
• LLM-powered task planner selects primitive skills
• Brain-inspired memory models adapt to home environments