AeroGrab：杂乱环境下的空中抓取统一框架
AeroGrab: A Unified Framework for Aerial Grasping in Cluttered Environments
AI总结：
提出一个集成主动探索、语言指令和碰撞感知抓取选择的端到端空中抓取框架，在杂乱环境中实现可靠抓取。
摘要：
由于遮挡和碰撞风险，在杂乱环境中可靠的空中抓取仍然具有挑战性。现有的空中操作管道主要依赖于基于质心的抓取，并且缺乏抓取姿态生成模型、主动探索和语言级任务规范之间的集成，导致缺乏完整的端到端系统。在这项工作中，我们提出了一个用于杂乱环境中可靠空中抓取的集成管道。给定一个场景和语言指令，系统识别目标对象并主动探索它以获得更好的对象视图。在探索过程中，抓取生成网络为每个视图预测多个6-DoF抓取候选。每个候选使用碰撞感知可行性框架进行评估，并使用标准轨迹生成和控制方法选择并执行整体最佳抓取。在杂乱真实世界场景中的实验证明了稳健可靠的抓取执行，突出了将主动感知与可行性感知抓取选择相结合用于空中操作的有效性。
英文摘要：
Reliable aerial grasping in cluttered environments remains challenging due to occlusions and collision risks. Existing aerial manipulation pipelines largely rely on centroid-based grasping and lack integration between the grasp pose generation models, active exploration, and language-level task specification, resulting in the absence of a complete end-to-end system. In this work, we present an integrated pipeline for reliable aerial grasping in cluttered environments. Given a scene and a language instruction, the system identifies the target object and actively explores it to gain better views of the object. During exploration, a grasp generation network predicts multiple 6-DoF grasp candidates for each view. Each candidate is evaluated using a collision-aware feasibility framework, and the overall best grasp is selected and executed using standard trajectory generation and control methods. Experiments in cluttered real-world scenarios demonstrate robust and reliable grasp execution, highlighting the effectiveness of combining active perception with feasibility-aware grasp selection for aerial manipulation.
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。
