This paper describes a strategy for implementing a robotic system capable of performing General Purpose Service Robot (GPSR) tasks in robocup@home. The GPSR task is that a real robot hears a variety of commands in spoken language and executes a task in a daily life environment. To achieve the task, we integrate foundation models based inference system and a state machine task executable. The foundation models plan the task and detect objects with open vocabulary, and a state machine task executable manages each robot's actions. This system works stable, and we took first place in the RoboCup@home Japan Open 2022's GPSR with 130 points, more than 85 points ahead of the other teams.

翻译：本文描述了一种在RoboCup@home赛事中实现通用服务机器人（GPSR）任务的机器人系统策略。GPSR任务要求真实机器人在日常生活环境中接收多种自然语言指令并执行相应操作。为实现该任务，我们整合了基于基础模型的推理系统与状态机任务执行模块。其中，基础模型负责进行开放词汇的任务规划与目标检测，而状态机任务执行模块则管理机器人的各项动作行为。该系统运行稳定，在2022年RoboCup@home日本公开赛的GPSR项目中以130分（领先第二名85分以上）的优异成绩获得第一名。