no code implementations • 27 May 2023 • Shuyue Lan, Zhilu Wang, Ermin Wei, Amit K. Roy-Chowdhury, Qi Zhu
We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.
1 code implementation • 31 Mar 2023 • YiXuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang, Jiajun Li, Xin Chen, Chao Huang, Wenchao Li, Qi Zhu
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
no code implementations • 29 Sep 2022 • YiXuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.
no code implementations • 15 Aug 2022 • Zhilu Wang, YiXuan Wang, Feisi Fu, Ruochen Jiao, Chao Huang, Wenchao Li, Qi Zhu
Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.
no code implementations • 26 Mar 2022 • Zhilu Wang, Chao Huang, Qi Zhu
The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations.
no code implementations • 28 Jan 2022 • YiXuan Wang, Simon Zhan, Zhilu Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu
In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e. g., safety, stability) under the learned controller.
2 code implementations • 25 Jun 2021 • Chao Huang, Jiameng Fan, Zhilu Wang, YiXuan Wang, Weichao Zhou, Jiajun Li, Xin Chen, Wenchao Li, Qi Zhu
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
no code implementations • 6 Jun 2021 • YiXuan Wang, Chao Huang, Zhaoran Wang, Zhilu Wang, Qi Zhu
Specifically, we leverage the verification results (computed reachable set of the system state) to construct feedback metrics for control learning, which measure how likely the current design of control parameters can meet the required reach-avoid property for safety and goal-reaching.
1 code implementation • 10 Aug 2020 • Shuyue Lan, Zhilu Wang, Amit K. Roy-Chowdhury, Ermin Wei, Qi Zhu
In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness.