3 citations · 5 across the 15 of their papers we have counts for
15 papers
ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract)
Guanwen Xie, Jingzehua Xu, Yiyuan Yang +2
We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom-environment, multi-objective learning-based methods (LB). ERFSL generates rewa…
AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)
Yimian Ding, Jingzehua Xu, Yiyuan Yang +3
Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process…
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…
When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks
Jingzehua Xu, Weiyi Liu, Weihang Zhang +4
Autonomous Underwater Vehicles (AUVs) are indispensable for marine exploration; yet, their control is hindered by nonlinear hydrodynamics, time-varying disturbances, and localizati…
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…
Never Too Rigid to Reach: Adaptive Virtual Model Control with LLM- and Lyapunov-Based Reinforcement Learning
Jingzehua Xu, Yangyang Li, Yangfei Chen +2
Robotic arms are increasingly deployed in uncertain environments, yet conventional control pipelines often become rigid and brittle when exposed to perturbations or incomplete info…
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…
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…
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
Yimian Ding, Jingzehua Xu, Guanwen Xie +2
This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in und…
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…
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions
Guanwen Xie, Jingzehua Xu, Yimian Ding +3
The adaptivity and maneuvering capabilities of Autonomous Underwater Vehicles (AUVs) have drawn significant attention in oceanic research, due to the unpredictable disturbances and…
Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?
Guanwen Xie, Jingzehua Xu, Ziqi Zhang +5
It is significant to employ multiple autonomous underwater vehicles (AUVs) to execute the underwater target tracking task collaboratively. However, it's pretty challenging to meet…
USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions
Jingzehua Xu, Guanwen Xie, Xinqi Wang +2
Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone o…
Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement
Guanwen Xie, Jingzehua Xu, Yiyuan Yang +2
Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and multiple requirements presents consider…
Enhancing Information Freshness: An AoI Optimized Markov Decision Process
Jingzehua Xu, Yimian Ding, Yiyuan Yang +2
Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly…