# Dynamical System-Based Imitation Learning and Neuroadaptive Control for Trajectory Recovery in Autonomous Ships

arXiv: https://arxiv.org/abs/2608.23924 · HTML: https://mlsanity.com/paper/2608.23924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 6 of 6)

- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [3D Perception based Imitation Learning under Limited Demonstration for Laparoscope Control in Robotic Surgery](https://mlsanity.com/paper/2204.03195/) (2022)
- [Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration](https://mlsanity.com/paper/2309.12856/) (2023)
- [Robust data-driven learning and control of nonlinear systems. A Sontag's formula approach](https://mlsanity.com/paper/2307.15662/) (2023)
- [Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles](https://mlsanity.com/paper/2501.09889/) (2025)

## Abstract (arXiv)

Repetitive maritime operations can be effectively learned using the Imitation Learning (IL) paradigm, which transfers human expertise directly to Unmanned Surface Vehicle (USV) control systems. Dynamical Systems (DS) are widely used to model non-linear human demonstrations while offering inherent stability guarantees. However, real-world execution under persistent marine perturbations reveals a critical trade-off: standard DS-based IL approaches prioritize global target convergence at the expense of localized trajectory reproduction fidelity. To address this limitation, we present a hybrid learning-control architecture that integrates a DS-based IL reference generator with a neuroadaptive controller. Our approach introduces a control action that drives the USV back to the demonstrated path following exogenous disturbances, enabling dynamic human-like reactive alignment-termed behavioral tracking. The proposed methodology is validated using the Marine Systems Simulator (MSS) toolbox. Simulation results confirm that the framework generalizes complex maneuvering tasks while substantially improving trajectory tracking fidelity under disturbances compared to alternative control strategies.