Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment using a single policy.
arXiv:2505. 08222v3 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking.
By Matteo Gallici, Ivan Masmitja, Mario Mart\'in
arXiv:2606. 08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control.
By Elisei Shafer, Oren Gal
The paper presents a curriculum‑based adversarial heterogeneous agent reinforcement learning (HARL‑AC) approach for autonomous quad‑copter landing on a ship deck in maritime settings. Using Heterogeneous‑Agent Proximal Policy Optimization (HAPPO) in NVIDIA Isaac Lab, the authors train a cooperative control policy that outperforms domain‑randomized baselines, achieving up to 97.5% success on in‑distribution sea states and higher median success and lower crash rates on out‑of‑distribution sea states. The adversarially trained policy also exhibits more cautious behavior, slightly increasing timeouts but improving safety in severe, unseen conditions.
By Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman, Kurt Driessens, Rico M\"ockel
arXiv:2603. 29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs).
By Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhenyu Wang, Chen An
arXiv:2608.20948v1 Announce Type: cross
Abstract: Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this...
By Zhitao Liu, Guangtong Xu, Zihan Wang, Jialiang Hou, Chao Xu, Fei Gao