Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks (2009.05613v2)

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

• The paper introduces a novel approach leveraging Graph Neural Networks to predict object importance, enabling efficient planning by focusing only on relevant elements.
• The GNN method predicts 'sufficient object sets' by analyzing object properties and relations, achieving consistently faster planning times across diverse domains.
• This approach offers significant implications for practical applications like robotics and theoretical advancements in neuro-symbolic AI by integrating neural prediction with symbolic planners.

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

The paper explores a novel approach to improving the efficiency of planning in large-scale real-world domains by leveraging Graph Neural Networks (GNNs) to predict the importance of objects within a system. A significant challenge within planning tasks is the sheer magnitude of potential objects, each of which may or may not be relevant to a specific goal. Traditional planning systems struggle under such complexity, requiring enhancements in how they perceive and process these tasks.

Core Insights and Methodology

In essence, the approach hinges on the idea of reducing the complexity of planning by intelligently limiting the set of objects considered. This reduction is achieved through a GNN-based architecture that predicts object importance, allowing planners to focus on only those elements that contribute significantly towards achieving the goal. The method operates with any off-the-shelf planner and makes no assumptions about the specifics of the planner employed or the transition model involved, highlighting the adaptability and applicability across various planning styles.

1. Graph Neural Networks for Object Importance:
   • The GNN architecture proposed is tasked with analyzing discrete and continuous properties of objects, and relations between them, to deduce their importance for planning.
   • By scoring objects individually and predicting which subsets are likely to be sufficient for solving a planning problem, this method addresses scalability and efficiency concerns inherent in planning over large object sets.
2. Sufficient Object Sets:
   • The paper defines a sufficient object set as one that allows the planner to derive a valid solution to the original planning problem when considering only these objects.
   • A learned model predicts these sets from a reduced representation of the problem instance, which is then validated incrementally through planning until a solution is found.
3. Experiments and Results:
   • Experimentation covers classical planning, probabilistic planning, and robotic task and motion planning domains.
   • Across various domains, their method consistently results in faster planning times compared to baseline methods, including traditional planners and learning-based grounding approaches.
   • Interestingly, this efficiency is achieved without significant overhead, ensuring completeness even when a large margin of error exists in object exclusion.

Implications and Future Directions

The methodological contributions of the paper are substantial for the domain of automated planning, providing a pathway to integrate neural prediction models with symbolic planners efficiently. The implications for future AI systems are significant:

• Practical Advancements: The application of this method to real-world systems like household robotics demonstrates potential practical applicability in scenarios where contextual understanding of an environment is critical for efficient task execution.
• Theoretical Extensions: Perfecting the utilization of GNNs in understanding relational dependencies and object importance paves the way for advancements in neuro-symbolic AI, a field that aspires to unify the statistical strength of deep learning with the symbolic reasoning abilities inherent in classical AI approaches.
• Learning Refinements: There's a natural curiosity in the community about refining the learning process further, perhaps utilizing variants of graph networks or exploring alternative relational models—each with specific biases that may improve accuracy and efficiency.

This exploration's strengths lie in its adaptability and generalizable solution framework. The insights from the empirical evaluations also underscore its promise in addressing computational constraints within complex domains, a pivotal step towards achieving robust AI systems. In advancing this work, researchers may explore a deeper integration of relational learning frameworks tailored to the unique structure and dynamics of large-scale environment modeling.