aiqbench analysis
This research advances aerial robotics by reframing propeller downwash from a disturbance into a controllable actuation mechanism for mobile manipulation. By training a reinforcement learning policy to exploit this airflow, the study demonstrates a novel capability for drones to dribble objects in real-world environments. The work represents a significant step forward in Unmanned Aerial Manipulator (UAM) dynamics and control strategies.
Drone Soccer: Learning to Manipulate with Multicopter Downwash
arXiv:2609.38588v1 Announce Type: new Abstract: Although multicopter drones are traditionally designed for "perception-only" tasks, like mapping and exploration, recent work has sought to develop Unmanned Aerial Manipulators (UAMs) to solve mobile manipulation tasks. Aerial manipulation performance can be impacted by "downwash," the airflow produced by propellers, but current state-of-the-art UAMs either ignore downwash or treat it as a disturbance. Instead, is it possible to actively use downwash as a tool during manipulation? We design a drone soccer task to explore the feasibility of downwash-based manipulation. Specifically, we develop a simplified downwash dynamics model which we use to train an RL policy to dribble a soccer ball. We further demonstrate that our policy transfers to real world deployment. This work provides key insights into novel manipulation capabilities for multicopters.
Abstract:Although multicopter drones are traditionally designed for "perception-only" tasks, like mapping and exploration, recent work has sought to develop Unmanned Aerial Manipulators (UAMs) to solve mobile manipulation tasks. Aerial manipulation performance can be impacted by "downwash," the airflow produced by propellers, but current state-of-the-art UAMs either ignore downwash or treat it as a disturbance. Instead, is it possible to actively use downwash as a tool during manipulation? We design a drone soccer task to explore the feasibility of downwash-based manipulation. Specifically, we develop a simplified downwash dynamics model which we use to train an RL policy to dribble a soccer ball. We further demonstrate that our policy transfers to real world deployment. This work provides key insights into novel manipulation capabilities for multicopters.
Robotics (cs.RO)
arXiv:2609.38588 [cs.RO]
(or arXiv:2609.38588v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.38588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 216 words
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