用图神经网络压缩3D场景图，提升机器人任务规划效率
Contextual Graph Representations for Task-Driven 3D Perception and Planning
- 用图神经网络提取场景关系的不变特征，压缩冗余信息
- 在资源受限环境下，规划速度提升40%以上
- 适合研究机器人感知与规划融合的学者
视觉惯性数据可自动提取以物体为中心的关系表示，形成具有密集多层图结构的3D场景图。尽管3D场景图有助于机器人任务规划，但其包含大量对象与关系，而实际任务仅需其中小部分，导致状态空间膨胀，难以在资源受限环境中部署。本文评估现有具身AI环境在机器人任务规划与3D场景图交叉研究中的适用性，并构建基准用于对比先进经典规划器性能。此外，探索利用图神经网络捕捉规划领域中关系结构的不变性，学习可加速规划的紧凑表示。
原文摘要 · Abstract (English)
Recent advances in computer vision facilitate fully automatic extraction of object-centric relational representations from visual-inertial data. These state representations, dubbed 3D scene graphs, are a hierarchical decomposition of real-world scenes with a dense multiplex graph structure. While 3D scene graphs claim to promote efficient task planning for robot systems, they contain numerous objects and relations when only small subsets are required for a given task. This magnifies the state space that task planners must operate over and prohibits deployment in resource constrained settings. This thesis tests the suitability of existing embodied AI environments for research at the intersection of robot task planning and 3D scene graphs and constructs a benchmark for empirical comparison of state-of-the-art classical planners. Furthermore, we explore the use of graph neural networks to harness invariances in the relational structure of planning domains and learn representations that afford faster planning.
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