A general-purpose service robot (GPSR), which can execute diverse tasks in various environments, requires a system with high generalizability and adaptability to tasks and environments. In this paper, we first developed a top-level GPSR system for worldwide competition (RoboCup@Home 2023) based on multiple foundation models. This system is both generalizable to variations and adaptive by prompting each model. Then, by analyzing the performance of the developed system, we found three types of failure in more realistic GPSR application settings: insufficient information, incorrect plan generation, and plan execution failure. We then propose the self-recovery prompting pipeline, which explores the necessary information and modifies its prompts to recover from failure. We experimentally confirm that the system with the self-recovery mechanism can accomplish tasks by resolving various failure cases. Supplementary videos are available at https://sites.google.com/view/srgpsr .

翻译：通用服务机器人（GPSR）需在多样环境中执行各类任务，这要求系统具备高度泛化能力与任务-环境自适应性。本文首先基于多种基础模型，为世界级竞赛（RoboCup@Home 2023）开发了顶层GPSR系统，该系统通过各模型的提示机制，既实现任务变体的泛化处理，又保持环境适应性。随后，通过分析实际运行表现，我们在更真实的GPSR应用场景中识别出三类故障：信息不足、规划错误及执行失败。为此提出自恢复提示流水线，通过探索缺失信息并动态修正提示词来恢复运行。实验证实，具备自恢复机制的该系统能有效应对各类故障案例完成任务。补充视频见 https://sites.google.com/view/srgpsr 。