48 citations · 192 across the 38 of their papers we have counts for
8 papers · 1 filter
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Charles Dawson, Zengyi Qin, Sicun Gao +1
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We devel…
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine +1
This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov fun…
Learning Density Distribution of Reachable States for Autonomous Systems
Yue Meng, Dawei Sun, Zeng Qiu +2
State density distribution, in contrast to worst-case reachability, can be leveraged for safety-related problems to better quantify the likelihood of the risk for potentially hazar…
Reactive and Safe Road User Simulations using Neural Barrier Certificates
Yue Meng, Zengyi Qin, Chuchu Fan
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can en…
Density Constrained Reinforcement Learning
Zengyi Qin, Yuxiao Chen, Chuchu Fan
We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by…
Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric
Dawei Sun, Mohammad Javad Khojasteh, Shubhanshu Shekhar +1
In this paper, we consider the problem of using a robot to explore an environment with an unknown, state-dependent disturbance function while avoiding some forbidden areas. The goa…
Optimal Mixed Discrete-Continuous Planning for Linear Hybrid Systems
Jingkai Chen, Brian Williams, Chuchu Fan
Planning in hybrid systems with both discrete and continuous control variables is important for dealing with real-world applications such as extra-planetary exploration and multi-v…
Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates
Zengyi Qin, Kaiqing Zhang, Yuxiao Chen +2
We study the multi-agent safe control problem where agents should avoid collisions to static obstacles and collisions with each other while reaching their goals. Our core idea is t…