用大模型简化复杂任务规划，让机器人更高效地完成家务。
Scalable Task Planning via Large Language Models and Structured World Representations
- 用大模型过滤无关状态，降低规划复杂度
- 在仿真和真实机械臂上均验证有效
- 适合需要智能决策的机器人系统
任务规划在大规模环境中面临计算不可行的问题。本文探索利用大语言模型（LLM）中编码的常识知识，增强规划方法应对复杂场景的能力。通过高效使用LLM对规划问题的状态空间进行剪枝，显著降低了其复杂性。我们在家庭仿真环境进行了大量实验，并通过一个7-DoF机械臂完成了真实世界验证（视频：https://youtu.be/6ro2UOtOQS4），证明了该系统的有效性。
原文摘要 · Abstract (English)
Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge encoded in LLMs to empower planning techniques to deal with these complex scenarios. We achieve this by efficiently using LLMs to prune irrelevant components from the planning problem's state space, substantially simplifying its complexity. We demonstrate the efficacy of this system through extensive experiments within a household simulation environment, alongside real-world validation using a 7-DoF manipulator (video https://youtu.be/6ro2UOtOQS4).
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