Problem
Research questions and friction points this paper is trying to address.
unmanned aerial vehicles
maritime environments
wind turbulence
ship-deck motion
Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings
🤖 AI Summary
研究通过使用基于课程和对抗性风代理的异构代理近端策略优化强化学习方法，解决海上环境中无人机自动着陆问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Curriculum-Based Adversarial Learning
Heterogeneous-Agent Proximal Policy Optimization (HAPPO)
Adversarial Wind Agent
Maritime Autonomous Landing
Generalization in Unseen Conditions
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