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.

SayPlan 是一种可扩展的、基于 LLM 的、大规模任务规划方法，使用 3D 场景图表示，在多层和多房间环境中对机器人的任务进行规划，其方法包括利用 3DSG 的分层结构和引入经典路径规划算法来降低规划范围，并使用迭代重新规划以在图形模拟器中反馈使行动可行和避免规划失败的信息。我们在两个大规模环境上进行了评估，显示了我们的方法能够从抽象和自然语言指令中为移动机械手执行大规模、长范围任务计划。