170 citations · 330 across the 27 of their papers we have counts for
31 papers
AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight
Xinhong Zhang, Qiyuan Zhu, Yubo Huang +8
Language-conditioned quadrotor flight requires a policy to ground semantic goals, anticipate the visual consequences of ego-motion, and output control references that remain smooth…
Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning
Yuzhou Wei, Wenjie Liu, Yifan Xie +3
Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing da…
MAD: Mapping-Aware World Models for Agile Quadrotor Flight
Xinhong Zhang, Runqing Wang, Yunfan Ren +6
Agile quadrotor flight in cluttered scenes requires more than a reactive mapping from a depth image to a control command: the vehicle must remember which regions have been observed…
EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
Tong Nie, Yuewen Mei, Yihong Tang +4
Generating safety-critical scenarios is essential for validating and improving autonomous driving systems, yet it inherently requires maximizing adversariality to expose failures w…
Mixture-of-World Models: Scaling Multi-Task Reinforcement Learning with Modular Latent Dynamics
Boxuan Zhang, Weipu Zhang, Zhaohan Feng +4
A fundamental challenge in multi-task reinforcement learning (MTRL) is achieving sample efficiency in visual domains where tasks exhibit substantial heterogeneity in both observati…
Data-driven control of network systems: Accounting for communication adaptivity and security
Gang Wang, Wenjie Liu, Yifei Li +3
Over the past decades, network systems have surged in significance, driven by merging technological advancements. These systems play pivotal roles in diverse applications ranging f…
DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao +5
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-bas…
DiffAero: A GPU-Accelerated Differentiable Simulation Framework for Efficient Quadrotor Policy Learning
Xinhong Zhang, Runqing Wang, Yunfan Ren +4
This letter introduces DiffAero, a lightweight, GPU-accelerated, and fully differentiable simulation framework designed for efficient quadrotor control policy learning. DiffAero su…
Multi-agent Embodied AI: Advances and Future Directions
Zhaohan Feng, Ruiqi Xue, Lei Yuan +7
Embodied artificial intelligence (Embodied AI) plays a pivotal role in the application of advanced technologies in the intelligent era, where AI systems are integrated with physica…
Data-driven Internal Model Control for Output Regulation
Wenjie Liu, Yifei Li, Jian Sun +4
Output regulation is a fundamental problem in control theory, extensively studied since the 1970s. Traditionally, research has primarily addressed scenarios where the system model…
Robust Offline Imitation Learning Through State-level Trajectory Stitching
Shuze Wang, Yunpeng Mei, Hongjie Cao +4
Imitation learning (IL) has proven effective for enabling robots to acquire visuomotor skills through expert demonstrations. However, traditional IL methods are limited by their re…
Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
Wei Xiao, Zhaohan Feng, Ziyu Zhou +3
This paper addresses the problem of guiding a quadrotor through a predefined sequence of waypoints in cluttered environments, aiming to minimize the flight time while avoiding coll…
Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments
Zhaohan Feng, Jie Chen, Wei Xiao +3
In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight con…
Distributed Data-driven Unknown-input Observers for State Estimation
Yuzhou Wei, Giorgia Disarò, Wenjie Liu +3
Unknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performa…
Robust Control of Unknown Switched Linear Systems from Noisy Data
Wenjie Liu, Yifei Li, Jian Sun +2
This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based st…
Data-driven Control Against False Data Injection Attacks
Wenjie Liu, Lidong Li, Jian Sun +3
The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-o…
Self-triggered Consensus Control of Multi-agent Systems from Data
Yifei Li, Xin Wang, Jian Sun +2
This paper considers self-triggered consensus control of unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advan…
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Weipu Zhang, Gang Wang, Jian Sun +2
Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized s…
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL
Yechen Zhang, Jian Sun, Gang Wang +2
Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games. How…
Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data
Yifei Li, Wenjie Liu, Jian Sun +3
This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies…
Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning
Runqing Wang, Gang Wang, Jian Sun +2
Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machi…
Efficient and Robust Time-Optimal Trajectory Planning and Control for Agile Quadrotor Flight
Ziyu Zhou, Gang Wang, Jian Sun +2
Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computatio…
Learning Robust Data-based LQG Controllers from Noisy Data
Wenjie Liu, Jian Sun, Gang Wang +2
This paper addresses the joint state estimation and control problems for unknown linear time-invariant systems subject to both process and measurement noise. The aim is to redesign…
EdgeYOLO: An Edge-Real-Time Object Detector
Shihan Liu, Junlin Zha, Jian Sun +2
This paper proposes an efficient, low-complexity and anchor-free object detector based on the state-of-the-art YOLO framework, which can be implemented in real time on edge computi…