Object placement is a fundamental component of aerial manipulation tasks, yet existing systems typically require the desired placement position to be specified explicitly in metric coordinates. Such interfaces are not intuitive and require users to reason about coordinate frames and scene geometry, making them difficult to use in practical deployments. In contrast, humans often communicate spatial goals through a combination of language and pointing gestures. Inspired by this observation, we present AERMANI-PLACE, a framework for language-guided object placement with aerial manipulators. Given a scene image and a natural language instruction, an image editing model generates a modified version of the scene containing a visual marker that indicates where the object should be placed. This marker is then grounded into the physical environment using depth observations to recover a metric place point, after which a placement trajectory is generated and executed by the aerial manipulator. We evaluate the proposed approach on a test set of 100 language-guided placement tasks and demonstrate successful execution on a real aerial manipulation platform. Experimental results show that the proposed method reliably infers placement locations from language instructions with an average success rate of 87\% on the test-set and transfers effectively to real-world aerial manipulation with an average success rate of 72\%. Video: https://youtu.be/SgwwgLBsv0g

翻译：物体放置是空中操作任务的基本组成部分，然而现有系统通常要求以公制坐标明确指定期望的放置位置。此类交互方式不够直观，需要用户理解坐标系和场景几何结构，使得实际部署中难以使用。相比之下，人类通常通过语言与指向手势的结合来传达空间目标。受这一观察启发，我们提出AERMANI-PLACE——一种基于语言引导的空中机械臂物体放置框架。给定场景图像和自然语言指令，图像编辑模型生成包含视觉标记的修改版场景，该标记指示物体应放置的位置。随后通过深度观测将此标记映射至物理环境，恢复公制放置点，最终由空中机械臂生成并执行放置轨迹。我们在包含100项语言引导放置任务的测试集上评估了所提方法，并在真实空中操作平台上成功执行。实验结果表明，所提方法能够从语言指令中可靠推断放置位置，测试集平均成功率为87%，在真实场景空中操作中平均成功率达72%。视频链接：https://youtu.be/SgwwgLBsv0g