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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.00889 |
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| _version_ | 1866913410496593920 |
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| author | Dimmig, Cora A. Kobilarov, Marin |
| author_facet | Dimmig, Cora A. Kobilarov, Marin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00889 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning Dimmig, Cora A. Kobilarov, Marin Robotics 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. |
| title | Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.00889 |