19 citations · 33 across the 6 of their papers we have counts for
7 papers
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Dongjie Yu, Wenjun Zou, Yujie Yang +4
Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such…
Synthesize Efficient Safety Certificates for Learning-Based Safe Control using Magnitude Regularization
Haotian Zheng, Haitong Ma, Sifa Zheng +2
Energy-function-based safety certificates can provide provable safety guarantees for the safe control tasks of complex robotic systems. However, all recent studies about learning-b…
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
Haitong Ma, Yang Guan, Shegnbo Eben Li +3
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsa…
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Haitong Ma, Jianyu Chen, Shengbo Eben Li +4
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks,…
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
Yang Guan, Yangang Ren, Qi Sun +5
Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learnin…
Feasibility Enhancement of Constrained Receding Horizon Control Using Generalized Control Barrier Function
Haitong Ma, Xiangteng Zhang, Shengbo Eben Li +3
Receding horizon control (RHC) is a popular procedure to deal with optimal control problems. Due to the existence of state constraints, optimization-based RHC often suffers the not…