@article{Ha2026, 
author = {Manh-Tuan Ha and Nhu-Nghia Bui and Dinh-Quy Vu and Thai-Viet Dang},
title = {Multi-View Latent Imitation Learning with Mamba-Based Action Encoding for Unmanned Surface Vehicle Navigation},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {1},
keywords = {Latent imitation learning, unmanned surface vehicles, latent space model, state-space models, multi-modal feature fusion},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.078280},
doi = {10.32604/cmc.2026.078280},
abstract = {The development of Unmanned Surface Vehicles (USVs) has become a key focus in marine robotics, fueling the need for navigation systems capable of performing complex and delicate tasks with speed and precision. However, the end-to-end path tracking process often encounters challenges in learning efficiency, and generalization, and varying environmental conditions. To achieve sample-efficient and robust USV navigation in dynamic maritime environments, the paper proposes a novel hierarchical multi-view latent imitation learning (IL) architecture. By formulating a latent IL objective, the framework disentangles diverse navigation modalities through continuous variables, preventing mode collapse and enhancing behavioral adaptability to non-stationary conditions. High-dimensional multi-view observations are transformed via a ViT-based backbone into compressed latent features to minimize redundant environmental information. These representations are processed by a Mamba-based action encoder, which leverages selective state-space modeling to capture long-term temporal dependencies with high computational efficiency. A UNet-based decoder subsequently forecasts optimal action sequences by synthesizing spatial maps to infer critical environment-agent relationships. This preliminary multi-view latent IL-based trajectory ensures precise tracking and dynamic stability while adhering to physical vehicle constraints. Experimental results validate that this end-to-end approach achieves robust path planning effectiveness, obstacle avoidance capability, and model training efficiency in complex, multi-modal maritime scenarios.}
}