Leveraging Pre-trained Large Language Models with Refined Prompting for Online Task and Motion Planning
This work addresses the need for robust and stable task execution in robotics, though it appears incremental by building on existing LLM and planning methods.
The paper tackles the problem of enabling intelligent robots to perform complex tasks by introducing LLM-PAS, a closed-loop system that uses a pre-trained Large Language Model for task planning and execution, with a First Look Prompting method to improve goal generation, demonstrating effectiveness in handling anomalies during execution.
With the rapid advancement of artificial intelligence, there is an increasing demand for intelligent robots capable of assisting humans in daily tasks and performing complex operations. Such robots not only require task planning capabilities but must also execute tasks with stability and robustness. In this paper, we present a closed-loop task planning and acting system, LLM-PAS, which is assisted by a pre-trained Large Language Model (LLM). While LLM-PAS plans long-horizon tasks in a manner similar to traditional task and motion planners, it also emphasizes the execution phase of the task. By transferring part of the constraint-checking process from the planning phase to the execution phase, LLM-PAS enables exploration of the constraint space and delivers more accurate feedback on environmental anomalies during execution. The reasoning capabilities of the LLM allow it to handle anomalies that cannot be addressed by the robust executor. To further enhance the system's ability to assist the planner during replanning, we propose the First Look Prompting (FLP) method, which induces LLM to generate effective PDDL goals. Through comparative prompting experiments and systematic experiments, we demonstrate the effectiveness and robustness of LLM-PAS in handling anomalous conditions during task execution.