AirDreamer: 基于世界模型的通用无人机导航
AirDreamer: Generalist Drone Navigation with World Models
AI总结：
提出一种结合强化学习策略和世界模型理解的无人机导航框架，通过稀疏奖励函数避免局部最优，在复杂未知环境中实现优于基线5.3%的成功率，并支持零调参的仿真到现实迁移。
摘要：
在未知且杂乱的环境中导航无人机需要可靠地泛化到未见过的场景布局，并理解与机器人能力相关的环境结构。先前的方法假设相同的环境配置，通常严重依赖人工设计的感知管道和预定义规则来引导机器人到达目标。这个过程依赖于环境，且跨环境泛化能力差。受动物导航行为启发，我们设计了一个导航框架，该框架在基于世界模型的环境理解之上使用基于强化学习的策略进行导航，以克服这些问题。此外，我们设计了一个无需手工塑造项的稀疏奖励函数，以避免局部极小值陷阱并鼓励偏航控制行为。在仿真和真实无人机上，我们的方法展现出在复杂未知环境中导航和逃离其他方法失败的局部最优的新兴能力。在具有挑战性的地图上，它比最佳基线实现了5.3%更高的导航成功率。此外，所提出的框架在部署期间无需任何调整即可实现有效的仿真到现实迁移。代码将公开。
英文摘要：
Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities. Previous methods, which assume the same environment configuration, often rely heavily on human-designed perception pipelines and predefined rules to guide the robot toward the target. This process is environment-dependent and generalizes poorly across environments. Inspired by animal navigation behavior, we design a navigation framework that navigates with a reinforcement-learning-based policy on top of a world-model-based environment understanding to overcome these issues. In addition, a sparse reward function without hand-crafted shaping terms is designed to avoid local minima traps and encourage yaw control behaviors. In simulation and on real drones, our method exhibits emergent capabilities for navigating complex, unseen environments and escaping local optima where other methods fail. In challenging maps, it achieves a 5.3% higher navigation success rate than best baseline. Furthermore, the proposed framework achieves effective sim-to-real transfer without any tuning during deployment. The code will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
