标题： 提示感知的视觉覆盖路径规划强化学习 显示英文标题

标题： Prompt Informed Reinforcement Learning for Visual Coverage Path Planning

摘要： 以无人飞行器（UAV）进行视觉覆盖路径规划需要代理战略性地协调UAV运动和相机控制，以最大化覆盖范围，最小化冗余，并保持电池效率。 传统的强化学习（RL）方法依赖于特定环境的奖励公式，缺乏语义适应性。 本研究提出了 提示引导强化学习（PIRL），这是一种将大型语言模型的零样本推理能力和上下文学习能力与好奇心驱动的RL相结合的新方法。 PIRL利用来自大型语言模型GPT-3.5的语义反馈，动态地调整指导代理在位置和相机调整中实现最佳视觉覆盖的近端策略优化（PPO）RL策略的奖励函数。 PIRL代理使用OpenAI Gym进行训练，并在各种环境中进行评估。 此外，通过在Webots模拟器中操作代理来测试代理的类似现实的仿真到现实的能力和零样本泛化能力，该模拟器引入了真实的物理动力学。 结果表明，PIRL优于多个基于学习的基线方法，如具有静态奖励的PPO、具有探索性权重初始化的PPO、模仿学习和仅使用LLM的控制器。 在不同的环境中，PIRL在OpenAI Gym中实现了比最佳基线高最多14%的视觉覆盖，在Webots中实现了高27%，在电池效率方面最高提高了25%，在冗余方面降低了最多18%，具体取决于环境。 结果突显了LLM引导的奖励设计在复杂空间探索任务中的有效性，并为将自然语言先验整合到机器人强化学习中提供了有前景的方向。 显示英文摘要

摘要： Visual coverage path planning with unmanned aerial vehicles (UAVs) requires agents to strategically coordinate UAV motion and camera control to maximize coverage, minimize redundancy, and maintain battery efficiency. Traditional reinforcement learning (RL) methods rely on environment-specific reward formulations that lack semantic adaptability. This study proposes Prompt-Informed Reinforcement Learning (PIRL), a novel approach that integrates the zero-shot reasoning ability and in-context learning capability of large language models with curiosity-driven RL. PIRL leverages semantic feedback from an LLM, GPT-3.5, to dynamically shape the reward function of the Proximal Policy Optimization (PPO) RL policy guiding the agent in position and camera adjustments for optimal visual coverage. The PIRL agent is trained using OpenAI Gym and evaluated in various environments. Furthermore, the sim-to-real-like ability and zero-shot generalization of the agent are tested by operating the agent in Webots simulator which introduces realistic physical dynamics. Results show that PIRL outperforms multiple learning-based baselines such as PPO with static rewards, PPO with exploratory weight initialization, imitation learning, and an LLM-only controller. Across different environments, PIRL outperforms the best-performing baseline by achieving up to 14% higher visual coverage in OpenAI Gym and 27% higher in Webots, up to 25% higher battery efficiency, and up to 18\% lower redundancy, depending on the environment. The results highlight the effectiveness of LLM-guided reward shaping in complex spatial exploration tasks and suggest a promising direction for integrating natural language priors into RL for robotics.