  Recent advancements in large language models (LLMs) have enabled a new
research domain, LLM agents, for solving robotics and planning tasks by
leveraging the world knowledge and general reasoning abilities of LLMs obtained
during pretraining. However, while considerable effort has been made to teach
the robot the "dos," the "don'ts" received relatively less attention. We argue
that, for any practical usage, it is as crucial to teach the robot the
"don'ts": conveying explicit instructions about prohibited actions, assessing
the robot's comprehension of these restrictions, and, most importantly,
ensuring compliance. Moreover, verifiable safe operation is essential for
deployments that satisfy worldwide standards such as ISO 61508, which defines
standards for safely deploying robots in industrial factory environments
worldwide. Aiming at deploying the LLM agents in a collaborative environment,
we propose a queryable safety constraint module based on linear temporal logic
(LTL) that simultaneously enables natural language (NL) to temporal constraints
encoding, safety violation reasoning and explaining, and unsafe action pruning.
To demonstrate the effectiveness of our system, we conducted experiments in
VirtualHome environment and on a real robot. The experimental results show that
our system strictly adheres to the safety constraints and scales well with
complex safety constraints, highlighting its potential for practical utility.
