Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning
<|reference_start|>Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning: With the continual adoption of Uncrewed Aerial Vehicles (UAVs) across a wide-variety of application spaces, robust aerial manipulation remains a key research challenge. Aerial manipulation tasks require interacting with objects in the environment, often without knowing their dynamical properties like mass and friction a priori. Additionally, interacting with these objects can have a significant impact on the control and stability of the vehicle. We investigated an approach for robust control and non-prehensile aerial manipulation in unknown environments. In particular, we use model-based Deep Reinforcement Learning (DRL) to learn a world model of the environment while simultaneously learning a policy for interaction with the environment. We evaluated our approach on a series of push tasks by moving an object between goal locations and demonstrated repeatable behaviors across a range of friction values.<|reference_end|>
@article{dimmig2024non-prehensile,
    title={Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement
  Learning},
    author={Cora A. Dimmig and Marin Kobilarov},
    journal={arXiv preprint arXiv:2407.00889},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00889},
    primaryClass={cs.RO}
}