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SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning

SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning

来源:Arxiv_logoArxiv
英文摘要

Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-floor, and multi-room environments presents a significant challenge for robotics. We introduce SayPlan, a scalable approach to LLM-based, large-scale task planning for robotics using 3D scene graph (3DSG) representations. To ensure the scalability of our approach, we: (1) exploit the hierarchical nature of 3DSGs to allow LLMs to conduct a 'semantic search' for task-relevant subgraphs from a smaller, collapsed representation of the full graph; (2) reduce the planning horizon for the LLM by integrating a classical path planner and (3) introduce an 'iterative replanning' pipeline that refines the initial plan using feedback from a scene graph simulator, correcting infeasible actions and avoiding planning failures. We evaluate our approach on two large-scale environments spanning up to 3 floors and 36 rooms with 140 assets and objects and show that our approach is capable of grounding large-scale, long-horizon task plans from abstract, and natural language instruction for a mobile manipulator robot to execute. We provide real robot video demonstrations on our project page https://sayplan.github.io.

Niko Suenderhauf、Ian Reid、Sourav Garg、Jesse Haviland、Krishan Rana、Jad Abou-Chakra

自动化技术、自动化技术设备计算技术、计算机技术自动化基础理论

Niko Suenderhauf,Ian Reid,Sourav Garg,Jesse Haviland,Krishan Rana,Jad Abou-Chakra.SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning[EB/OL].(2023-07-12)[2025-05-31].https://arxiv.org/abs/2307.06135.点此复制

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