Open Access
ARTICLE
Multi-View Latent Imitation Learning with Mamba-Based Action Encoding for Unmanned Surface Vehicle Navigation
Manh-Tuan Ha1, Nhu-Nghia Bui2, Dinh-Quy Vu1,*, Thai-Viet Dang2,*
1 Department of Vehicle and Energy Conversion Engineering, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam
2 Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam
* Corresponding Author: Dinh-Quy Vu. Email: 
; Thai-Viet Dang. Email: 
(This article belongs to the Special Issue: Intelligent Perception, Decision-making and Security Control for Unmanned Systems in Complex Environments)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.078280
Received 28 December 2025; Accepted 15 April 2026; Published online 29 April 2026
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.
Keywords
Latent imitation learning; unmanned surface vehicles; latent space model; state-space models; multi-modal feature fusion
Login
Register
Submit a Paper
Propose a Special lssue
Download PDF
Downloads
Citation Tools
