用大模型辅助规划，在部分已知环境中更高效地找物品
Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection
- 用大模型估算不同位置找到目标的概率，结合地图成本做智能搜索决策
- 仿真中比纯大模型策略快11.8%，比乐观策略快39.2%
- 部署时快速选最优提示和大模型，减少6.5%平均成本
我们提出一种新型的基于大模型（LLM）的模型化规划框架及提示选择方法，用于在部分已知环境中进行物体搜索。该方法利用大模型估算在场景各位置搜索目标物体的可能性，结合从环境地图中提取的移动成本，构建搜索模型，从而以大模型信息指导规划，实现高效搜索。此外，该方法依赖的抽象结构可借助近期离线回放技术实现在部署时的模型选择，我们借此实现了部署阶段快速选择最优提示与大模型。仿真实验表明，相比完全依赖大模型的基线策略，本方法性能提升达11.8%；相比乐观策略，提升达39.2%。所提出的类赌博机选择机制使提示与大模型的选择速度更快，平均成本降低6.5%，平均累积后悔值下降33.8%。真实机器人在公寓环境中的实验也验证了上述改进，进一步支持该方法的有效性。
原文摘要 · Abstract (English)
We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to estimate statistics about the likelihood of finding the target object when searching various locations throughout the scene that, combined with travel costs extracted from the environment map, are used to instantiate a model, thus using the LLM to inform planning and achieve effective search performance. Moreover, the abstraction upon which our approach relies is amenable to deployment-time model selection via the recent offline replay approach, an insight we leverage to enable fast prompt and LLM selection during deployment. Simulation experiments demonstrate that our LLM-informed model-based planning approach outperforms the baseline planning strategy that fully relies on LLM and optimistic strategy with as much as 11.8% and 39.2% improvements respectively, and our bandit-like selection approach enables quick selection of best prompts and LLMs resulting in 6.5% lower average cost and 33.8% lower average cumulative regret over baseline UCB bandit selection. Real-robot experiments in an apartment demonstrate similar improvements and so further validate our approach.
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