Task and Motion Planning in Hierarchical 3D Scene Graphs

Fuente: arXiv
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Autori principali: Ray, Aaron, Bradley, Christopher, Carlone, Luca, Roy, Nicholas
Natura: Preprint
Pubblicazione: 2024
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author Ray, Aaron
Bradley, Christopher
Carlone, Luca
Roy, Nicholas
author_facet Ray, Aaron
Bradley, Christopher
Carlone, Luca
Roy, Nicholas
contents Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however an open question is how to derive a planning domain from a 3D scene graph that enables efficient computation of executable plans. In this work, we present a novel approach for defining and solving Task and Motion Planning problems in large-scale environments using hierarchical 3D scene graphs. We describe a method for building sparse problem instances which enables scaling planning to large scenes, and we propose a technique for incrementally adding objects to that domain during planning time that minimizes computation on irrelevant elements of the scene graph. We evaluate our approach in two real scene graphs built from perception, including one constructed from the KITTI dataset. Furthermore, we demonstrate our approach in the real world, building our representation, planning in it, and executing those plans on a real robotic mobile manipulator. A video supplement is available at \url{https://youtu.be/v8fkwLjBn58}.
format Preprint
id arxiv_https___arxiv_org_abs_2403_08094
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Task and Motion Planning in Hierarchical 3D Scene Graphs
Ray, Aaron
Bradley, Christopher
Carlone, Luca
Roy, Nicholas
Robotics
68T40, 68T20
I.2.9; I.2.4; I.2.8
Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however an open question is how to derive a planning domain from a 3D scene graph that enables efficient computation of executable plans. In this work, we present a novel approach for defining and solving Task and Motion Planning problems in large-scale environments using hierarchical 3D scene graphs. We describe a method for building sparse problem instances which enables scaling planning to large scenes, and we propose a technique for incrementally adding objects to that domain during planning time that minimizes computation on irrelevant elements of the scene graph. We evaluate our approach in two real scene graphs built from perception, including one constructed from the KITTI dataset. Furthermore, we demonstrate our approach in the real world, building our representation, planning in it, and executing those plans on a real robotic mobile manipulator. A video supplement is available at \url{https://youtu.be/v8fkwLjBn58}.
title Task and Motion Planning in Hierarchical 3D Scene Graphs
topic Robotics
68T40, 68T20
I.2.9; I.2.4; I.2.8
url https://arxiv.org/abs/2403.08094