  Enabling robots to understand language instructions and react accordingly to
visual perception has been a long-standing goal in the robotics research
community. Achieving this goal requires cutting-edge advances in natural
language processing, computer vision, and robotics engineering. Thus, this
paper mainly investigates the potential of integrating the most recent Large
Language Models (LLMs) and existing visual grounding and robotic grasping
system to enhance the effectiveness of the human-robot interaction. We
introduce the WALL-E (Embodied Robotic WAiter load lifting with Large Language
model) as an example of this integration. The system utilizes the LLM of
ChatGPT to summarize the preference object of the users as a target instruction
via the multi-round interactive dialogue. The target instruction is then
forwarded to a visual grounding system for object pose and size estimation,
following which the robot grasps the object accordingly. We deploy this
LLM-empowered system on the physical robot to provide a more user-friendly
interface for the instruction-guided grasping task. The further experimental
results on various real-world scenarios demonstrated the feasibility and
efficacy of our proposed framework. See the project website at:
https://star-uu-wang.github.io/WALL-E/
