Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback

Understand

Planning algorithms decompose complex problems into intermediate steps that can be sequentially executed by robots to complete tasks.

• Recent works have employed Large Language Models (LLMs) for task planning, using natural language to generate robot policies in both simulation and real-world environments.
• LLMs like GPT-4 have shown promising results in generalizing to unseen tasks, but their applicability is limited due to hallucinations caused by insufficient grounding in the robot environment.
• The robustness of LLMs in task planning can be enhanced with environmental state information and feedback.