53 citations · 105 across the 13 of their papers we have counts for
13 papers
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach
Sungwook Yang, Chaoying Pei, Ran Dai +1
Sampling-based model predictive control (MPC) has found significant success in optimal control problems with non-smooth system dynamics and cost function. Many machine learning-bas…
On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approach
Alaa Eddine Chriat, Chuangchuang Sun
Safety has been a critical issue for the deployment of learning-based approaches in real-world applications. To address this issue, control barrier function (CBF) and its variants…
An efficient approach for nonconvex semidefinite optimization via customized alternating direction method of multipliers
Chuangchuang Sun
We investigate a class of general combinatorial graph problems, including MAX-CUT and community detection, reformulated as quadratic objectives over nonconvex constraints and solve…
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation
Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How
In a multirobot system, a number of cyber-physical attacks (e.g., communication hijack, observation perturbations) can challenge the robustness of agents. This robustness issue wor…
Reachability Analysis of Neural Feedback Loops
Michael Everett, Golnaz Habibi, Chuangchuang Sun +1
Neural Networks (NNs) can provide major empirical performance improvements for closed-loop systems, but they also introduce challenges in formally analyzing those systems' safety p…
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
Aris Kanellopoulos, Filippos Fotiadis, Chuangchuang Sun +4
In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specif…