2 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Performance Index Shaping for Closed-loop Optimal Control
Ayush Rai, Shaoshuai Mou, Brian D. O. Anderson
The design of the performance index, also referred to as cost or reward shaping, is central to both optimal control and reinforcement learning, as it directly determines the behavi…
Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft
Hao Tu, Yebin Wang, Shaoshuai Mou +1
Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery…
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…
Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
Shunan Yin, Zehui Lu, Shaoshuai Mou
This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and motion characteristics are unknown…
Reward-Based Collision-Free Algorithm for Trajectory Planning of Autonomous Robots
Jose D. Hoyos, Tianyu Zhou, Zehui Lu +1
This paper proposes a novel mission planning algorithm for autonomous robots that selects an optimal waypoint sequence from a predefined set to maximize total reward while satisfyi…