  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.
