用大模型分解任务，结合符号规划提升机器人规划速度与成功率
Fast and Accurate Task Planning using Neuro-Symbolic Language Models and Multi-level Goal Decomposition
- 用大模型将复杂任务拆解为子目标，降低规划难度
- 在不同复杂度子目标上灵活选择符号或蒙特卡洛树搜索规划器
- 在真实与仿真环境中均实现快速且高成功率的机器人任务规划
在机器人任务规划中，基于规则的符号规划器（如PDDL）虽有效，但在复杂环境中的长序列任务因搜索空间指数增长而效率低下。而基于大语言模型（LLM）的方法虽推理快、具备常识推理能力，但成功率较低。为此，本文提出一种新型神经符号任务规划方法：利用大模型对复杂任务进行多层级子目标分解，并根据子目标复杂度，选择符号规划或基于蒙特卡洛树搜索（MCTS）的LLM规划器分别处理。该策略显著缩小搜索空间，使大模型聚焦于更易管理的任务，从而大幅降低规划时间，同时保持高成功率。实验表明，该方法在多个任务规划领域及真实与仿真机器人环境中均表现优异。更多信息见 http://graphics.ewha.ac.kr/LLMTAMP/。
原文摘要 · Abstract (English)
In robotic task planning, symbolic planners using rule-based representations like PDDL are effective but struggle with long-sequential tasks in complicated environments due to exponentially increasing search space. Meanwhile, LLM-based approaches, which are grounded in artificial neural networks, offer faster inference and commonsense reasoning but suffer from lower success rates. To address the limitations of the current symbolic (slow speed) or LLM-based approaches (low accuracy), we propose a novel neuro-symbolic task planner that decomposes complex tasks into subgoals using LLM and carries out task planning for each subgoal using either symbolic or MCTS-based LLM planners, depending on the subgoal complexity. This decomposition reduces planning time and improves success rates by narrowing the search space and enabling LLMs to focus on more manageable tasks. Our method significantly reduces planning time while maintaining high success rates across task planning domains, as well as real-world and simulated robotics environments. More details are available at http://graphics.ewha.ac.kr/LLMTAMP/.
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