17 citations · 17 across the 11 of their papers we have counts for
11 papers
Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks
Xinglong Zhang, Qingwen Ma, Cong Li +5
Distributed learning control for multirobot systems (MRS) offers significant flexibility in presence of uncertainties but lacks provable performance guarantees. A promising directi…
Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination
Jiaqi Li, Xinglong Zhang, Haibin Xie +3
Latent world models improve sample efficiency in continuous control by optimizing policies over imagined latent trajectories, but common neural transitions offer limited direct con…
Robust Koopman MPC with Sets Updates for Time Delayed Systems
Xinglong Zhang, Xinxin Yao, Xin Xu +2
Koopman operators have shown significant potential in designing linear model predictive control (MPC) schemes for nonlinear systems on a lifted observable space. Recent advances ha…
Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning
Xinglong Zhang, Yongqian Xiao, Haotian Cao +3
Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need for accurate models and onli…
Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations
Xinglong Zhang, Cong Li, Hangjie Mo +13
Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stiffness, and flexibly adjustin…
TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design
Juan Du, Yueteng Wu, Pan Zhao +4
The aerodynamic design of turbomachinery is a complex and tightly coupled multi-stage process involving geometry generation, performance prediction, optimization, and high-fidelity…
Diffusion Policies with Value-Conditional Optimization for Offline Reinforcement Learning
Yunchang Ma, Tenglong Liu, Yixing Lan +4
In offline reinforcement learning, value overestimation caused by out-of-distribution (OOD) actions significantly limits policy performance. Recently, diffusion models have been le…
Versatile Distributed Maneuvering with Generalized Formations using Guiding Vector Fields
Yang Lu, Sha Luo, Pengming Zhu +4
This paper presents a unified approach to realize versatile distributed maneuvering with generalized formations. Specifically, we decompose the robots' maneuvers into two independe…
Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC
Xinglong Zhang, Wei Pan, Cong Li +4
Distributed model predictive control (DMPC) is promising in achieving optimal cooperative control in multirobot systems (MRS). However, real-time DMPC implementation relies on nume…
Vector Field-Guided Learning Predictive Control for Motion Planning of Mobile Robots with Uncertain Dynamics
Yang Lu, Weijia Yao, Yongqian Xiao +4
In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding ve…
DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles
Yongqian Xiao
Autonomous driving has attracted lots of attention in recent years. An accurate vehicle dynamics is important for autonomous driving techniques, e.g. trajectory prediction, motion…