25 citations · 38 across the 14 of their papers we have counts for
14 papers
Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems
Wenjian Hao, Yuxuan Fang, Zehui Lu +1
This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed approach learns linear lifted dyn…
Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics
Wenjian Hao, Yuxuan Fang, Zehui Lu +1
This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of cla…
A Control-Barrier-Function-Based Algorithm for Policy Adaptation in Reinforcement Learning
Wenjian Hao, Zehui Lu, Nicolas Miguel +1
This paper considers the problem of adapting a predesigned policy, represented by a parameterized function class, from a solution that minimizes a given original cost function to a…
Distributed Koopman Learning using Partial Trajectories for Control
Wenjian Hao, Zehui Lu, Devesh Upadhyay +1
This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, eac…
Distributed Koopman Learning with Incomplete Measurements
Wenjian Hao, Lili Wang, Ayush Rai +1
Koopman operator theory has emerged as a powerful tool for system identification, particularly for approximating nonlinear time-invariant systems (NTIS). This paper considers a net…
Deep Koopman Learning using Noisy Data
Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou
This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under nois…
C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
Jianwen Li, Hyunsang Park, Wenjian Hao +10
In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions.…
Adaptive Policy Learning to Additional Tasks
Wenjian Hao, Zehui Lu, Zihao Liang +2
This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradien…
Optimal Control of Nonlinear Systems with Unknown Dynamics
Wenjian Hao, Paulo C. Heredia, Shaoshuai Mou
This paper presents a data-driven method to find a closed-loop optimal controller, which minimizes a specified infinite-horizon cost function for systems with unknown dynamics. Sup…
A Data-Driven Approach for Inverse Optimal Control
Zihao Liang, Wenjian Hao, Shaoshuai Mou
This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its…
Deep Koopman Learning of Nonlinear Time-Varying Systems
Wenjian Hao, Bowen Huang, Wei Pan +2
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the…
Deep Learning of Koopman Representation for Control
Yiqiang Han, Wenjian Hao, Umesh Vaidya
We develop a data-driven, model-free approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopm…
Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments
Wenjian Hao, Rongyao Wang, Alexander Krolicki +1
Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environme…
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach
Wenjian Hao, Yiqiang Han
This paper compares two different types of data-driven control methods, representing model-based and model-free approaches. One is a recently proposed method - Deep Koopman Represe…