SayPlan: Grounding Large Language Models using 3D Scene Graphs for
  Scalable Task Planning
Authors: name: Krishan Rana, name: Jesse Haviland, name: Sourav Garg, name: Jad Abou-Chakra, name: Ian Reid, name: Niko Suenderhauf

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, 36 rooms and
140 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.