  The recent breakthroughs in the research on Large Language Models (LLMs) have
triggered a transformation across several research domains. Notably, the
integration of LLMs has greatly enhanced performance in robot Task And Motion
Planning (TAMP). However, previous approaches often neglect the consideration
of dynamic environments, i.e., the presence of dynamic objects such as humans.
In this paper, we propose a novel approach to address this gap by incorporating
human awareness into LLM-based robot task planning. To obtain an effective
representation of the dynamic environment, our approach integrates humans'
information into a hierarchical scene graph. To ensure the plan's
executability, we leverage LLMs to ground the environmental topology and
actionable knowledge into formal planning language. Most importantly, we use
LLMs to predict future human activities and plan tasks for the robot
considering the predictions. Our contribution facilitates the development of
integrating human awareness into LLM-driven robot task planning, and paves the
way for proactive robot decision-making in dynamic environments.
