collaborators

6 papers

cs.RO2026

EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control

Guanwen Xie, Jingzehua Xu, Jiwei Tang +6

Despite recent advances in Unmanned Underwater Vehicle (UUV) attitude control, existing methods still struggle with generalizability, robustness to real-world disturbances, and eff…

cs.RO2026

When Semantics Connect the Swarm: LLM-Driven Fuzzy Control for Cooperative Multi-Robot Underwater Coverage

Jingzehua Xu, Weihang Zhang, Yangyang Li +5

Underwater multi-robot cooperative coverage remains challenging due to partial observability, limited communication, environmental uncertainty, and the lack of access to global loc…

cs.RO2026

Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment

Jingzehua Xu, Guanwen Xie, Jiwei Tang +2

Autonomous underwater robots are increasingly deployed for environmental monitoring, infrastructure inspection, subsea resource exploration, and long-horizon exploration. Yet, desp…

cs.RO2025

Never too Cocky to Cooperate: An FIM and RL-based USV-AUV Collaborative System for Underwater Tasks in Extreme Sea Conditions

Jingzehua Xu, Guanwen Xie, Jiwei Tang +5

This paper develops a novel unmanned surface vehicle (USV)-autonomous underwater vehicle (AUV) collaborative system designed to enhance underwater task performance in extreme sea c…

eess.SY2025

LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Driving

Yiming Shu, Jiahui Xu, Jiwei Tang +2

Autonomous highway driving demands a critical balance between proactive, efficiency-seeking behavior and robust safety guarantees. This paper proposes Language Action-guided Reinfo…

cs.RO2025

Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks

Jingzehua Xu, Guanwen Xie, Weiyi Liu +6

Autonomous Underwater Vehicles (AUVs) are essential for marine exploration, yet their control remains highly challenging due to nonlinear dynamics and uncertain environmental distu…