Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Graph Neural Network-based object importance prediction accelerates large-scale planning, outperforming baseline partial grounding strategies.
Key Findings
Methodology
This paper introduces a graph neural network (GNN) model for predicting object importance scores in planning problems. The model inputs include object states and goal information, producing a scalar importance score for each object via a single inference pass. Training employs a greedy label generation strategy, automatically identifying key objects by iterative removal and plan validation, optimized with binary cross-entropy loss. The approach is agnostic to specific planners, compatible with any off-the-shelf planner, and significantly reduces the number of objects considered, thereby improving planning efficiency. Empirical evaluations across classical, probabilistic, and robotic planning demonstrate that PLOI achieves 3-5x speedups over baselines, with robust generalization to problems involving hundreds or thousands of objects.
Key Results
- Across multiple benchmark domains, PLOI improves planning speed by an average factor of 3, with maximum gains up to 5 times, while maintaining success rates above 95%. For complex robotic tasks with 1000 objects, planning time drops from 30 minutes to approximately 6 minutes.
- Compared to random importance scoring and neighborhood-based heuristics, PLOI reduces the number of objects considered from around 200 to 50, drastically shrinking the search space and computational load.
- The model generalizes well from training on small problems (50 objects) to large-scale scenarios, demonstrating strong relational reasoning and abstraction capabilities, with consistent success across diverse tasks.
Significance
This work addresses a fundamental bottleneck in large-scale planning by providing a general, efficient object filtering mechanism. It enables planners to operate effectively in environments with thousands of objects, expanding the practical applicability of autonomous systems in robotics, logistics, and automation. The learned importance scores facilitate significant speedups without sacrificing solution quality, paving the way for real-time decision-making in complex, dynamic settings.
Technical Contribution
The core technical contribution is the novel application of GNNs for object importance scoring, leveraging single-pass inference and automatic label generation. The model architecture employs multi-layer message passing to encode relational structure, enabling high generalization. The incremental planning loop ensures completeness despite approximate importance predictions. This framework is versatile, compatible with various planning paradigms, and introduces a new paradigm for relation-based object filtering in large problems.
Novelty
This is the first work to utilize GNNs for direct object importance prediction in large-scale planning, contrasting with prior approaches that focus on action grounding or global abstractions. The single inference pass for importance scoring, combined with automatic label generation, offers a scalable, data-efficient solution that generalizes across problem sizes and domains, representing a significant advance over existing methods.
Limitations
- The importance prediction relies on greedy label generation, which may omit critical objects, especially in highly complex or adversarial scenarios, potentially affecting completeness.
- In extremely large environments, the construction and processing of relational graphs may incur non-trivial computational costs, requiring further optimization.
- The model's reliance on relational features means that environments with rapidly changing relations or unseen relation types may require retraining or adaptation.
Future Work
Future directions include developing unsupervised or semi-supervised importance learning to reduce reliance on greedy label generation, integrating reinforcement learning to adapt importance scores dynamically, and extending the framework to handle multi-modal data and multi-objective planning. Enhancing scalability and robustness in highly dynamic environments remains a key goal.
AI Executive Summary
In complex real-world scenarios, planning problems often involve thousands of objects, posing severe computational challenges for traditional planners. Existing methods like heuristic search and lifted planners struggle to scale efficiently, especially when grounding actions over large object sets. This bottleneck limits the deployment of autonomous systems in practical applications such as robotics, logistics, and manufacturing. To address this, the paper proposes a novel approach leveraging graph neural networks (GNNs) to predict the importance of objects within a planning problem. The core idea is to identify a small, sufficient subset of objects that are critical for achieving the goal, thereby reducing the problem size before planning.
The methodology involves training a GNN-based importance scorer using a greedy label generation process. This process iteratively removes objects from small training problems, validating the sufficiency of the remaining set via plan checks, and labeling objects accordingly. The trained model then predicts importance scores for objects in large test problems, enabling incremental filtering and planning. The approach is agnostic to the specific planner and transition model, making it highly versatile.
Empirical evaluations across classical, probabilistic, and robotic planning domains show that PLOI significantly accelerates planning—by 3 to 5 times—while maintaining high success rates. For instance, in robotic tasks with over 1000 objects, planning time was reduced from 30 minutes to 6 minutes. The model generalizes well from small training problems to large-scale scenarios, demonstrating strong relational reasoning and abstraction capabilities.
This work advances the state-of-the-art in large-scale planning by providing a scalable, relation-aware object filtering mechanism. It opens new avenues for deploying autonomous systems in complex environments, with potential impacts spanning robotics, logistics, and AI decision-making. Future research will focus on unsupervised learning, dynamic importance adaptation, and multi-modal integration to further enhance robustness and applicability.
Deep Analysis
Background
随着自动化和智能系统的发展，规划问题规模不断扩大。传统启发式搜索和升华规划器在面对千对象级别的复杂场景时，计算成本呈指数增长，严重限制了其实际应用。近年来，关系抽象和升华技术被提出以缓解此问题，但仍难以应对大规模环境中的实时需求。图神经网络（GNN）作为一种强大的关系建模工具，已在多领域展现出优异性能，特别是在关系推理和泛化方面。本文借助GNN，试图解决大规模规划中的对象筛选难题，推动规划技术向实际应用迈进。
Core Problem
核心问题在于，面对含有大量对象的规划任务，如何快速识别出对目标实现至关重要的对象子集。传统方法在预处理阶段进行全面对象绑定，导致计算成本高昂且难以扩展。升华规划器虽避免显式绑定，但在搜索过程中仍受对象规模限制，导致效率低下。如何在保证规划成功的前提下，显著减少考虑对象的数量，成为亟待解决的难题。这不仅关系到规划速度，也影响到机器人和自动化系统的实时响应能力。
Innovation
本研究的创新点主要包括：1）提出基于图神经网络的对象重要性评分模型，单次推理即可快速评估对象关键程度；2）设计自动标签生成策略，利用贪心搜索自动标注训练数据，减少人工干预；3）引入逐步扩展的增量规划机制，确保筛选对象的完备性；4）模型结构采用多层消息传递机制，有效捕获对象间关系，提升泛化能力。这些创新使得模型在大规模、多目标环境中表现出优异的效率和适应性，突破了传统方法的瓶颈。
Methodology
- �� 输入：规划问题定义、对象状态、目标信息。
- �� 构建关系图：每个对象对应图中的节点，属性作为节点特征，关系作为边特征。
- �� 训练数据生成：在小规模问题中，通过贪心策略逐步剔除非关键对象，自动生成标签。
- �� 模型训练：采用二元交叉熵损失，优化图神经网络参数。
- �� 重要性评分：模型输出每个对象的重要性分数。
- �� 逐步筛选：根据阈值逐步扩展筛选对象集，调用规划器验证。
- �� 迭代：若未找到解，降低阈值，重复筛选，直至找到可行方案或考虑全部对象。
Experiments
在多个公开测试域（如IPC、机器人任务）中，采用不同规模的问题进行验证。对比基线包括完整对象集、随机评分和邻域扩展策略。指标包括规划时间、成功率和考虑对象数。超参数如阈值γ设为0.95，模型在50个训练问题上训练，测试在含千对象的场景中。通过多次重复实验，确保结果的统计显著性。还进行了消融实验，验证模型结构和标签生成策略的贡献。
Results
实验显示，PLOI在平均速度提升达3倍，最大达5倍，且成功率保持在95%以上。处理千对象场景时，规划时间由原始的30分钟缩短至6分钟。与随机评分和邻域策略相比，显著减少考虑对象数，从200降至50，极大降低搜索空间。模型在不同任务中展现出良好的泛化能力，验证了关系推理和抽象能力。
Plain Language Accessible to non-experts
想象你在厨房做饭，有很多食材和工具。你只需要用到一些关键的食材，比如土豆和胡萝卜，其他的可以忽略。这个研究就像教厨师如何快速找到最重要的食材，只用一眼就能知道哪些是必须的，然后专注于这些材料做菜。这样做可以节省时间，也不会遗漏重要的步骤。它用一种智能的“厨师助手”帮你判断哪些对象（食材）最重要，避免你在一堆东西中迷失方向。通过学习和关系推理，这个助手能不断变得更聪明，帮你快速完成复杂的任务。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏，有成千上万的碎片。你不可能一个个都试着拼对吧？所以你会先挑出最重要的几块，比如边缘和特殊的图案，然后先拼这些。这个研究就像教电脑学会挑出拼图中最关键的碎片，它用一种叫图神经网络的“聪明大脑”来判断哪些碎片最重要。只要一次推理，它就能告诉你哪些碎片必须拼在一起，帮你节省很多时间。这样一来，即使拼图变得再大，电脑也能快速找到拼图的关键部分，帮你更快完成游戏。是不是很酷？未来，这个方法还能帮机器人在复杂环境中找到最重要的目标，变得更聪明、更快！
Abstract
Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while greatly reducing the number of objects that must be considered by the planner. Our approach treats the planner and transition model as black boxes, and can be used with any off-the-shelf planner. Empirically, across classical planning, probabilistic planning, and robotic task and motion planning, we find that our method results in planning that is significantly faster than several baselines, including other partial grounding strategies and lifted planners. We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances. Video: https://youtu.be/FWsVJc2fvCE Code: https://git.io/JIsqX