SINGER：面向无人机的机载通用视觉-语言导航策略
SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones
AI总结：
SINGER提出一种仅依赖机载感知与计算的开放词汇无人机语言导航策略，通过高斯泼溅仿真器、RRT启发式多轨迹专家和轻量端到端视觉运动策略实现零样本实机迁移，并在到达率、视野保持率和碰撞率上显著优于基线。
摘要：
大型视觉-语言模型推动了开放词汇机器人策略的显著进展，例如通用机器人操作策略，使机器人能够完成以自然语言指定的复杂任务。尽管取得了这些成功，开放词汇自主无人机导航仍然是一个未解决的挑战，原因在于大规模演示数据稀缺、无人机稳定控制的实时性要求以及缺乏可靠的外部位姿估计模块。在本工作中，我们提出SINGER，用于在开放世界中仅使用机载感知和计算实现语言引导的自主无人机导航。为了训练鲁棒的开放词汇导航策略，SINGER利用三个核心组成部分：（i）一个具有最小仿真到现实差距的照片级真实感语言嵌入飞行模拟器，使用高斯泼溅（Gaussian Splatting）进行高效数据生成；（ii）一个受RRT启发的多轨迹生成专家，用于生成无碰撞导航演示；这些用于训练（iii）一个轻量级端到端视觉运动策略，用于实时闭环控制。通过大量硬件飞行实验，我们证明了该策略在未见环境和未见语言条件目标物体上具有优越的零样本仿真到现实迁移能力。当在约70万到100万个语言条件视觉运动数据的观测-动作对上训练并部署到硬件上时，SINGER在到达查询目标方面平均比速度控制的语义引导基线高出23.33%，在将查询目标保持在视野内方面平均高出16.67%，并且碰撞减少10%。
英文摘要：
Large vision-language models have driven remarkable progress in open-vocabulary robot policies, e.g., generalist robot manipulation policies, that enable robots to complete complex tasks specified in natural language. Despite these successes, open-vocabulary autonomous drone navigation remains an unsolved challenge due to the scarcity of large-scale demonstrations, real-time control demands of drones for stabilization, and lack of reliable external pose estimation modules. In this work, we present SINGER for language-guided autonomous drone navigation in the open world using only onboard sensing and compute. To train robust, open-vocabulary navigation policies, SINGER leverages three central components: (i) a photorealistic language-embedded flight simulator with minimal sim-to-real gap using Gaussian Splatting for efficient data generation, (ii) an RRT-inspired multi-trajectory generation expert for collision-free navigation demonstrations, and these are used to train (iii) a lightweight end-to-end visuomotor policy for real-time closed-loop control. Through extensive hardware flight experiments, we demonstrate superior zero-shot sim-to-real transfer of our policy to unseen environments and unseen language-conditioned goal objects. When trained on ~700k-1M observation action pairs of language conditioned visuomotor data and deployed on hardware, SINGER outperforms a velocity-controlled semantic guidance baseline by reaching the query 23.33% more on average, and maintains the query in the field of view 16.67% more on average, with 10% fewer collisions.
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。
