Real-world task planning requires long-horizon reasoning over large sets of objects with complex relationships and attributes, leading to a combinatorial explosion for classical symbolic planners. To prune the search space, recent methods prioritize searching on a simplified task only containing a few ``important" objects predicted by a neural network. However, such a simple neuro-symbolic (NeSy) integration risks omitting critical objects and wasting resources on unsolvable simplified tasks. To enable Fast and reliable planning, we introduce a NeSy relaxation strategy (Flax), combining neural importance prediction with symbolic expansion. Specifically, we first learn a graph neural network to predict object importance to create a simplified task and solve it with a symbolic planner. Then, we solve a rule-relaxed task to obtain a quick rough plan, and reintegrate all referenced objects into the simplified task to recover any overlooked but essential elements. Finally, we apply complementary rules to refine the updated task, keeping it both reliable and compact. Extensive experiments are conducted on both synthetic and real-world maze navigation benchmarks where a robot must traverse through a maze and interact with movable obstacles. The results show that Flax boosts the average success rate by 20.82\% and cuts mean wall-clock planning time by 17.65\% compared with the state-of-the-art NeSy baseline. We expect that Flax offers a practical path toward fast, scalable, long-horizon task planning in complex environments.

翻译：现实世界的任务规划需要对具有复杂关系和属性的大规模对象集合进行长程推理，这导致经典符号规划器面临组合爆炸问题。为剪枝搜索空间，现有方法优先在仅包含神经网络预测的少数"重要"对象的简化任务上进行搜索。然而，这种简单的神经符号（NeSy）集成方式可能遗漏关键对象，并在不可解的简化任务上浪费计算资源。为实现快速可靠的任务规划，我们提出一种结合神经重要性预测与符号扩展的神经符号松弛策略（Flax）。具体而言，我们首先训练图神经网络预测对象重要性以构建简化任务，并用符号规划器求解该任务。随后，我们求解规则松弛任务以获得快速粗略规划，并将所有被引用的对象重新整合到简化任务中以恢复可能被忽略但关键的元素。最后，我们应用互补规则对更新后的任务进行优化，使其保持可靠性与紧凑性。我们在合成与真实世界迷宫导航基准测试中进行了大量实验，其中机器人必须穿越迷宫并与可移动障碍物交互。实验结果表明，相较于最先进的神经符号基线方法，Flax将平均成功率提升了20.82%，并将平均实际规划时间降低了17.65%。我们预期Flax能为复杂环境中的快速、可扩展、长程任务规划提供实用路径。