Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

2026-07-02 · Source: Machine Learning · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

An adaptive reinforcement learning approach is presented for trajectory tracking in autonomous surface vehicles (ASVs), enabling zero-shot cross-platform deployment with a single policy. This method addresses cross-platform generalization by conditioning on interaction history, utilizing a teacher-student architecture to infer a latent representation of platform dynamics. The policy is trained in simulation under randomized vessel dynamics using a simple analytical model, not a high-fidelity hydrodynamic simulator. Real-world experiments on two distinct platforms demonstrated that the adaptive policy outperforms non-adaptive learning-based baselines by up to 58% in position mean absolute error, achieving tracking accuracy comparable to platform-specific tuned controllers without any fine-tuning.

Key takeaway

For Robotics Engineers developing autonomous surface vehicle controllers, you should consider adaptive reinforcement learning with a teacher-student architecture to achieve zero-shot cross-platform deployment. This approach significantly improves trajectory tracking accuracy on diverse platforms without fine-tuning, outperforming non-adaptive methods by up to 58%. Implementing randomized dynamics training in simulation can enhance your policy's generalization capabilities, reducing the need for extensive platform-specific tuning efforts.

Key insights

Adaptive reinforcement learning enables zero-shot cross-platform control for diverse autonomous surface vehicles.

Principles

• Conditioning on interaction history addresses partial observability.
• Randomized dynamics training improves policy generalization.

Method

Train a teacher-student RL policy in simulation with randomized vessel dynamics, inferring latent platform dynamics from interaction history for zero-shot cross-platform deployment.

In practice

• Employ a teacher-student architecture for dynamic inference.
• Train policies with randomized dynamics for robustness.

Topics

• Autonomous Surface Vehicles
• Reinforcement Learning
• Cross-Platform Control
• Trajectory Tracking
• Teacher-Student Architecture
• Robotics

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.