The development of a general purpose service robot for daily life necessitates the robot's ability to deploy a myriad of fundamental behaviors judiciously. Recent advancements in training Large Language Models (LLMs) can be used to generate action sequences directly, given an instruction in natural language with no additional domain information. However, while the outputs of LLMs are semantically correct, the generated task plans may not accurately map to acceptable actions and might encompass various linguistic ambiguities. LLM hallucinations pose another challenge for robot task planning, which results in content that is inconsistent with real-world facts or user inputs. In this paper, we propose a task planning method based on a constrained LLM prompt scheme, which can generate an executable action sequence from a command. An exceptional handling module is further proposed to deal with LLM hallucinations problem. This module can ensure the LLM-generated results are admissible in the current environment. We evaluate our method on the commands generated by the RoboCup@Home Command Generator, observing that the robot demonstrates exceptional performance in both comprehending instructions and executing tasks.

翻译：开发适用于日常生活的通用服务机器人，需要机器人能够审慎地部署多种基本行为。近期大型语言模型（LLMs）的训练进展使得模型能够直接根据自然语言指令（无需额外领域信息）生成动作序列。然而，尽管LLMs的输出在语义上是正确的，但生成的任务规划可能无法准确映射到可执行动作，且可能包含多种语言歧义。LLM幻觉问题对机器人任务规划提出了另一挑战，这会导致生成内容与现实世界事实或用户输入不一致。本文提出一种基于受限LLM提示方案的任务规划方法，能够从指令生成可执行的动作序列。为进一步处理LLM幻觉问题，我们提出了异常处理模块，该模块可确保LLM生成的结果在当前环境中是可采纳的。我们在RoboCup@Home指令生成器产生的指令上评估了所提方法，观察到机器人在指令理解和任务执行方面均表现出卓越性能。