{"ID":3004919,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T10:21:46.366257699Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03441","arxiv_id":"2606.03441","title":"PerchRL: Vision-Based Agile Perching on Inclined Platforms under Rapid and Irregular Motion","abstract":"Autonomous vision-based perching of quadrotors on moving inclined platforms is critical for air-ground collaboration but remains challenging due to the limited field of view (FOV). In this paper, we propose PerchRL, a reinforcement learning (RL) framework for vision-based agile perching on inclined platforms under rapid and irregular motion. Specifically, we employ a two-stage learning strategy consisting of state-based pre-training followed by vision-based fine-tuning. To improve generalization across diverse platform motions, we employ randomized platform trajectories to prevent overfitting and temporal augmentation methods to capture latent motion patterns from historical observations. During vision-based fine-tuning, a hybrid learning framework consisting of visibility-aware state augmentation and active perception rewards is presented to improve robustness under intermittent visual loss. Extensive simulation and real-world experiments demonstrate the feasibility, stability, and real-time performance of PerchRL, while successful deployment across distinct quadrotor platforms further validates its adaptability. The source code will be released to benefit the community.","short_abstract":"Autonomous vision-based perching of quadrotors on moving inclined platforms is critical for air-ground collaboration but remains challenging due to the limited field of view (FOV). In this paper, we propose PerchRL, a reinforcement learning (RL) framework for vision-based agile perching on inclined platforms under rapi...","url_abs":"https://arxiv.org/abs/2606.03441","url_pdf":"https://arxiv.org/pdf/2606.03441v1","authors":"[\"Zihong Lu\",\"Zongzhuo Liu\",\"Huaxu Li\",\"Jinqiang Cui\",\"Jie Mei\",\"Youmin Gong\",\"U Kei Cheang\",\"Boyu Zhou\"]","published":"2026-06-02T10:26:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}