From the 1 of 9 linked papers with an AI index.
9 papers
Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics
Wenjian Hao, Yuxuan Fang, Zehui Lu +1
The paper proposes a model predictive path integral control method that replaces costly nonlinear dynamics with a learned linear deep Koopman operator, enabling faster trajectory s…
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…
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…
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 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 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…