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Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings — arXiv:2609.12758

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12758
License (as archived): CC0-1.0
Archived: 2026-10-16T03:15:49.757Z
SHA-256 of the archived text: 75e6f343f6716d1eddc50183b01103f8467af0ad02aa4fba4ffc7d81ffe12f09
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How to cite

Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings — arXiv:2609.12758 — arXiv (en), https://arxiv.org/abs/2609.12758 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aeb71ca3ffbd (Metraipolis archive dc_aeb71ca3ffbd, sha256 75e6f343f6716d1eddc50183b01103f8467af0ad02aa4fba4ffc7d81ffe12f09)

BibTeX

@misc{dcaeb71ca3ffbd,
  title = {Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings — arXiv:2609.12758},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12758},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aeb71ca3ffbd; sha256 75e6f343f6716d1eddc50183b01103f8467af0ad02aa4fba4ffc7d81ffe12f09},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12758. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recovering unmanned aerial vehicles (UAVs) in maritime environments is challenging due to wind turbulence and ship-deck motion, making it a valuable test case for alternative control and learning approaches as conventional landing approaches often become unreliable. We study simulated mid-air capture of quadrotor UAVs by a ship-mounted robotic arm, learning robust cooperative control policies with Heterogeneous-Agent Proximal Policy Optimization (HAPPO) Reinforcement Learning. We train with HAPPO using a curriculum and an adversarial wind agent (HARL-AC) in NVIDIA Isaac Lab, and compare the obtained control policies against those generated through curriculum-based domain randomization and a benchmark trained on a single sea state. In-distribution evaluation on sea states $0/4/5$ shows comparable success for HARL-AC and domain randomization of up to $97.5\%$. On out-of-distribution sea states $7/8/10$, HARL-AC generalizes better, achieving up to $16\%$ higher median success rate at sea state 10, and substantially lower crash rates of up to $14\%$ compared to the domain randomization policy. Furthermore, we show that the adversarially trained policy shows more cautious behavior, slightly increasing timeouts by $<3\%$, but yields safer recovery behavior in severe, unseen conditions.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.12758 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T03:05:44.062Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).