arXiv:2604.12645v2 Announce Type: replace-cross Abstract: Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles under highly uncertain and non-stationary underwater dynamics. To address these challenges, we employ a data-driven reinforcement learning approach to compensate for unknown dynamics and task variations. Traditional single-task reinforcement learning has a tendency to overfit the training environment, thus, limit the long-term usefulness of the learnt policy. H
Advances in reinforcement learning and autonomous underwater vehicle technology are converging, making sophisticated marine monitoring feasible, particularly in the context of increasing climate change awareness.
This development represents a significant step towards scalable, data-driven environmental monitoring, enabling better understanding and management of critical marine ecosystems and supporting broader climate and ecological initiatives.
The ability to deploy autonomous systems for complex, dynamic underwater tasks with improved resilience to environmental uncertainty changes the paradigm for marine data collection and ecosystem assessment.
- · Environmental monitoring agencies
- · Marine robotics companies
- · Climate scientists
- · AI research institutions
- · Traditional manual marine survey methods
- · Organizations reliant on infrequent or limited marine data
Autonomous underwater vehicles become more effective and widely adopted for various marine applications beyond monitoring.
The abundance of high-resolution marine data drives new discoveries in oceanography and accelerates marine conservation efforts.
Sophisticated AI-driven autonomous systems become standard for managing and protecting critical global natural resources, reducing human intervention in hazardous environments.
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Read at arXiv cs.AI