Hibikino-Musashi@Home 2024 Team Description Paper
Kosei Isomoto, Akinobu Mizutani, Fumiya Matsuzaki, Hikaru, Sato, Ikuya Matsumoto, Kosei Yamao, Takuya Kawabata, Tomoya Shiba, and Yuga Yano, Atsuki Yokota, Daiju Kanaoka, Hiromasa Yamaguchi and, Kazuya Murai, Kim Minje, Lu Shen, Mayo Suzuka, Moeno Anraku and, Naoki Yamaguchi
TL;DR
This paper describes the Hibikino-Musashi@Home team's approach to developing a home service robot using a dataset generator, open-source tools, and a large language model for task planning, aiming to assist humans in domestic environments.
Contribution
The paper introduces a comprehensive system combining dataset generation, open-source development, and LLM-based task planning for home service robots.
Findings
Developed a dataset generator for robot vision training
Created an open-source environment on a Human Support Robot simulator
Implemented a large language model-powered task planner
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 has 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 users. The team aims to design a home service robot that can assist humans in their homes and continuously attends competitions to evaluate and improve the developed system.
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Taxonomy
TopicsMultimodal Machine Learning Applications · Robotics and Automated Systems · Social Robot Interaction and HRI