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

arXiv: https://arxiv.org/abs/2607.02037 · HTML: https://mlsanity.com/paper/2607.02037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Preparing for the Unknown: Learning a Universal Policy with Online System Identification](https://mlsanity.com/paper/1702.02453/) (2017)
- [Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs](https://mlsanity.com/paper/2110.05038/) (2021)
- [GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots](https://mlsanity.com/paper/2209.05309/) (2022)
- [ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots](https://mlsanity.com/paper/2310.10486/) (2023)
- [COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning](https://mlsanity.com/paper/2006.09540/) (2020)
- [Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters](https://mlsanity.com/paper/2302.08100/) (2023)
- [Roboat II: A Novel Autonomous Surface Vessel for Urban Environments](https://mlsanity.com/paper/2007.10220/) (2020)
- [Solgenia -- A Test Vessel Toward Energy-Efficient Autonomous Water Taxi Applications](https://mlsanity.com/paper/2502.01207/) (2025)

## Abstract (arXiv)

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 which a learned module infers a latent representation of the platform dynamics. The policy is trained in simulation under randomized vessel dynamics and is deployed zero-shot to two real-world platforms without any fine-tuning, despite relying on a simple analytical dynamics model rather than a high-fidelity hydrodynamic simulator. In real-world experiments on two different platforms, the adaptive policy outperforms non-adaptive learning-based baselines by up to 58% in position mean absolute error while approaching the tracking accuracy of a platform-specific tuned controller.