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/.

翻译：在机器人任务规划领域，采用如PDDL等基于规则表示的符号规划器虽然有效，但在复杂环境中处理长序列任务时，由于搜索空间呈指数级增长而面临困难。与此同时，基于人工神经网络的LLM方法虽能提供更快的推理速度和常识推理能力，但其成功率较低。为克服当前符号方法（速度慢）与基于LLM方法（精度低）的局限，我们提出了一种新颖的神经符号任务规划器：该方法利用LLM将复杂任务分解为子目标，并根据子目标的复杂度，选择采用符号规划器或基于蒙特卡洛树搜索的LLM规划器为每个子目标执行任务规划。这种分解机制通过缩小搜索空间并使LLM专注于更易处理的任务，从而减少规划时间并提高成功率。我们的方法在多个任务规划领域以及真实世界与模拟机器人环境中，均能在保持高成功率的同时显著缩短规划时间。更多细节请访问 http://graphics.ewha.ac.kr/LLMTAMP/。