arXiv:2607.02037v1 Announce Type: cross Abstract: 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. Since the deployment platform's dynamics are unknown to the policy, we address cross-platform generalization with the standard partial-observability approach of conditioning on interaction history, employing a teacher-student architecture in whic
The proliferation of various autonomous vehicle platforms and the demand for versatile control systems are driving innovation in adaptive AI approaches.
This breakthrough in adaptive reinforcement learning for autonomous surface vehicles (ASVs) demonstrates significant progress towards more generalized and robust AI control systems.
Current platform-specific controllers for ASVs could be replaced by more versatile, zero-shot deployable policies, reducing development time and increasing operational flexibility.
- · Autonomous vehicle manufacturers
- · Defence contractors
- · Maritime logistics companies
- · AI software developers
- · Developers of highly specialized, single-platform control systems
- · Companies with less adaptive AI development capabilities
Increased efficiency and reduced cost in deploying diverse fleets of autonomous surface vehicles across varied environments.
Accelerated development and adoption of other types of autonomous vehicles (aerial, ground) leveraging similar adaptive AI principles.
Potential for a significant military advantage for nations deploying such advanced autonomous capabilities in maritime domains.
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Read at arXiv cs.LG