# Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks

arXiv: https://arxiv.org/abs/2009.05613 · HTML: https://mlsanity.com/paper/2009.05613/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 30 of 38)

- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [Learning Multi-Object Dynamics with Compositional Neural Radiance Fields](https://mlsanity.com/paper/2202.11855/) (2022)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning](https://mlsanity.com/paper/2105.14074/) (2021)
- [Efficient and Interpretable Robot Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2102.13177/) (2021)
- [Dynamic-Resolution Model Learning for Object Pile Manipulation](https://mlsanity.com/paper/2306.16700/) (2023)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning](https://mlsanity.com/paper/2211.01576/) (2022)
- [STAP: Sequencing Task-Agnostic Policies](https://mlsanity.com/paper/2210.12250/) (2022)
- [Learning to Search in Task and Motion Planning with Streams](https://mlsanity.com/paper/2111.13144/) (2021)
- [MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning](https://mlsanity.com/paper/2505.03035/) (2025)
- [PG3: Policy-Guided Planning for Generalized Policy Generation](https://mlsanity.com/paper/2204.10420/) (2022)
- [Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures](https://mlsanity.com/paper/2306.05171/) (2023)
- [Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery](https://mlsanity.com/paper/2203.04120/) (2022)
- [MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds](https://mlsanity.com/paper/2312.12891/) (2023)
- [Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning](https://mlsanity.com/paper/2406.10479/) (2024)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search](https://mlsanity.com/paper/2506.07062/) (2025)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [SLAP: Shortcut Learning for Abstract Planning](https://mlsanity.com/paper/2511.01107/) (2025)
- [Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks](https://mlsanity.com/paper/2305.04692/) (2023)
- [Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks](https://mlsanity.com/paper/2203.15030/) (2022)
- [Policy-Guided Lazy Search with Feedback for Task and Motion Planning](https://mlsanity.com/paper/2210.14055/) (2022)
- [Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning](https://mlsanity.com/paper/2409.19226/) (2024)
- [WLPlan: Relational Features for Symbolic Planning](https://mlsanity.com/paper/2411.00577/) (2024)
- [Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2603.08814/) (2026)
- [StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation](https://mlsanity.com/paper/2609.20791/) (2026)
- [A Categorical Representation Language and Computational System for Knowledge-Based Planning](https://mlsanity.com/paper/2305.17208/) (2023)

## Abstract (arXiv)

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