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

• URL: http://arxiv.org/abs/2307.06135v2
• Date: Wed, 27 Sep 2023 23:17:28 GMT
• Title: SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
• Authors: Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid and Niko Suenderhauf
• Abstract summary: We introduce SayPlan, a scalable approach to large-scale task planning for robotics using 3D scene graph (3DSG) representations. We evaluate our approach on two large-scale environments spanning up to 3 floors and 36 rooms with 140 assets and objects.
• Score: 15.346150968195015
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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