用语言指令引导机器人在救援中高效探索，提升决策与学习速度。
Selective Exploration and Information Gathering in Search and Rescue Using Hierarchical Learning Guided by Natural Language Input
- 通过大模型解析人类语言，动态调整分层强化学习策略
- 在长周期稀疏奖励环境中，学习效率显著提升
- 适合需要人机协作的复杂救援场景
近年来，机器人和自主系统在日常生活中的应用日益广泛，为多个领域提供复杂问题的解决方案。然而，在搜救（SAR）行动中，其应用面临独特挑战：由于地形广阔、环境剧变及时间紧迫，全面探索灾区往往不可行。传统机器人系统通常依赖预设搜索模式，无法有效利用来自人类相关方提供的真实信息，而这些信息恰恰能加速学习进程并提升伤员分类效率。为此，本文提出一种结合大型语言模型（LLMs）社会交互与分层强化学习（HRL）框架的系统。该系统可将人类相关方的口头输入转化为可执行的强化学习洞察，并据此调整搜索策略。通过利用大模型获取的人类信息与分层强化学习结构化任务执行，本方法不仅弥合了自主能力与人类智能之间的差距，还显著提升了智能体在长时程、稀疏奖励环境下的学习效率与决策能力。
原文摘要 · Abstract (English)
In recent years, robots and autonomous systems have become increasingly integral to our daily lives, offering solutions to complex problems across various domains. Their application in search and rescue (SAR) operations, however, presents unique challenges. Comprehensively exploring the disaster-stricken area is often infeasible due to the vastness of the terrain, transformed environment, and the time constraints involved. Traditional robotic systems typically operate on predefined search patterns and lack the ability to incorporate and exploit ground truths provided by human stakeholders, which can be the key to speeding up the learning process and enhancing triage. Addressing this gap, we introduce a system that integrates social interaction via large language models (LLMs) with a hierarchical reinforcement learning (HRL) framework. The proposed system is designed to translate verbal inputs from human stakeholders into actionable RL insights and adjust its search strategy. By leveraging human-provided information through LLMs and structuring task execution through HRL, our approach not only bridges the gap between autonomous capabilities and human intelligence but also significantly improves the agent's learning efficiency and decision-making process in environments characterised by long horizons and sparse rewards.
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