用大模型提升机器人任务规划的抗错能力
Leveraging Pre-trained Large Language Models with Refined Prompting for Online Task and Motion Planning
- 将约束检查后移至执行阶段，实现动态反馈
- 提出FLP提示方法，生成更有效的任务目标
- 适合需要应对环境异常的智能机器人研发
随着人工智能快速发展，对能协助人类完成日常任务并执行复杂操作的智能机器人需求日益增长。这类机器人不仅需具备任务规划能力，还需在执行中保持稳定与鲁棒性。本文提出一种闭环任务规划与执行系统 LLM-PAS，借助预训练大语言模型（LLM）实现。该系统在长期任务规划上类似传统规划器，但更关注执行阶段：将部分约束检查从规划阶段转移到执行阶段，使系统能探索约束空间，并在执行中对环境异常提供更精确反馈。大模型的推理能力可处理鲁棒执行器无法应对的异常情况。为增强系统在重规划中的辅助能力，本文提出首看提示（FLP）方法，引导大模型生成有效 PDDL 目标。通过对比提示实验与系统性实验，验证了 LLM-PAS 在处理执行异常时的有效性与鲁棒性。
原文摘要 · Abstract (English)
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.
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