  Large Language Models (LLMs) have been recently used in robot applications
for grounding LLM common-sense reasoning with the robot's perception and
physical abilities. In humanoid robots, memory also plays a critical role in
fostering real-world embodiment and facilitating long-term interactive
capabilities, especially in multi-task setups where the robot must remember
previous task states, environment states, and executed actions. In this paper,
we address incorporating memory processes with LLMs for generating cross-task
robot actions, while the robot effectively switches between tasks. Our proposed
dual-layered architecture features two LLMs, utilizing their complementary
skills of reasoning and following instructions, combined with a memory model
inspired by human cognition. Our results show a significant improvement in
performance over a baseline of five robotic tasks, demonstrating the potential
of integrating memory with LLMs for combining the robot's action and perception
for adaptive task execution.
