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This research introduces AERMANI-PLACE, a framework that enables aerial manipulators to execute object placement tasks using natural language instructions rather than explicit coordinate inputs. By leveraging an image editing model to generate visual markers from text prompts and grounding them via depth observations, the system achieves high success rates in both simulation and real-world deployments. The work advances robotic autonomy by bridging intuitive human-robot interaction with precise physical manipulation capabilities.
AERMANI-PLACE: Language Guided Object Placement with Aerial Manipulators
arXiv:2606.14531v1 Announce Type: new Abstract: 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
Abstract: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: this https URL
Robotics (cs.RO)
arXiv:2606.14531 [cs.RO]
(or arXiv:2606.14531v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14531 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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