TY  - EJOU
AU  - Ha, Manh-Tuan 
AU  - Bui, Nhu-Nghia 
AU  - Vu, Dinh-Quy 
AU  - Dang, Thai-Viet 

TI  - Multi-View Latent Imitation Learning with Mamba-Based Action Encoding for Unmanned Surface Vehicle Navigation
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - 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.
KW  - Latent imitation learning; unmanned surface vehicles; latent space model; state-space models; multi-modal feature fusion

DO  - 10.32604/cmc.2026.078280