用大模型做机器人规划，闭环控制更可靠
Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks
- 把大模型当作闭环控制器，实时调整决策
- 缩短控制周期能显著提升任务成功率
- 适合想用大模型做智能机器人的开发者
大型语言模型（LLMs）和视觉语言模型（VLMs）已广泛应用于具身高阶规划。然而，在黑箱环境中部署时常导致不可预测或代价高昂的错误。为更可靠地利用其能力，我们实证研究了将语言模型作为闭环规划器的实际策略。具体而言，我们考察了控制时域长度和预热启动对语言模型规划性能的影响。通过设计并执行受控实验，提取可操作的洞察，提出有助于提升语言模型驱动具身规划性能与鲁棒性的建议。完整实现与实验均在项目网站上公开。
原文摘要 · Abstract (English)
Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often leads to unpredictable or costly errors. To harness their capabilities more reliably in robotic systems, we empirically investigate practical strategies for integrating language models as closed-loop planners. Concretely, we study how the control horizon and warm-starting impact the performance of language model-based planners. We design and conduct controlled experiments to extract actionable insights, providing recommendations that can help improve the performance and robustness of language model-based embodied planning. The full implementation and experiments are available on the project website
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