1 citations · 2 across the 8 of their papers we have counts for
8 papers
Online Intention Prediction via Control-Informed Learning
Tianyu Zhou, Zihao Liang, Zehui Lu +1
This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynam…
Safe Online Control-Informed Learning
Tianyu Zhou, Zihao Liang, Zehui Lu +1
This paper proposes a Safe Online Control-Informed Learning framework for safety-critical autonomous systems. The framework unifies optimal control, parameter estimation, and safet…
Online Control-Informed Learning
Zihao Liang, Tianyu Zhou, Zehui Lu +1
This paper proposes an Online Control-Informed Learning (OCIL) framework, which employs the well-established optimal control and state estimation techniques in the field of control…
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…
An Iterative Method to Learn a Linear Control Barrier Function
Zihao Liang, Jason King Ching Lo
Control barrier function (CBF) has recently started to serve as a basis to develop approaches for enforcing safety requirements in control systems. However, constructing such funct…
Learning Objective Functions Incrementally by Inverse Optimal Control
Zihao Liang, Wanxin Jin, Shaoshuai Mou
This paper proposes an inverse optimal control method which enables a robot to incrementally learn a control objective function from a collection of trajectory segments. By saying…