27 citations · 89 across the 18 of their papers we have counts for
18 papers
Variational Delayed Policy Optimization
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +5
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning…
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +6
Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either s…
State-Wise Safe Reinforcement Learning With Pixel Observations
Simon Sinong Zhan, Yixuan Wang, Qingyuan Wu +3
In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety vio…
Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Yixuan Wang, Ruochen Jiao, Sinong Simon Zhan +5
Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of t…
Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
Ruochen Jiao, Yixuan Wang, Xiangguo Liu +2
Trajectory generation and trajectory prediction are two critical tasks in autonomous driving, which generate various trajectories for testing during development and predict the tra…
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
Yixuan Wang, Weichao Zhou, Jiameng Fan +6
Neural networks (NNs) playing the role of controllers have demonstrated impressive empirical performances on challenging control problems. However, the potential adoption of NN con…
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao +6
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…
A Tool for Neural Network Global Robustness Certification and Training
Zhilu Wang, Yixuan Wang, Feisi Fu +4
With the increment of interest in leveraging machine learning technology in safety-critical systems, the robustness of neural networks under external disturbance receives more and…
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding
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 sens…
Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner
Xiangguo Liu, Chao Huang, Yixuan Wang +2
Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performan…
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
Yixuan Wang, Simon Zhan, Zhilu Wang +4
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 control…
Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees
Yixuan Wang, Chao Huang, Zhaoran Wang +2
In the current control design of safety-critical autonomous systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the r…
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
Chao Huang, Jiameng Fan, Zhilu Wang +6
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs). Existing approaches that le…
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation
Yixuan Wang, Chao Huang, Zhilu Wang +3
Neural networks are being increasingly applied to control and decision-making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without r…
Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems
Yixuan Wang, Chao Huang, Qi Zhu
Neural networks have been increasingly applied for control in learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great promises in improving system performance and…
SAW: A Tool for Safety Analysis of Weakly-hard Systems
Chao Huang, Kai-Chieh Chang, Chung-Wei Lin +1
We introduce SAW, a tool for safety analysis of weakly-hard systems, in which traditional hard timing constraints are relaxed to allow bounded deadline misses for improving design…
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
Chao Huang, Shichao Xu, Zhilu Wang +3
Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverag…
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
Chao Huang, Jiameng Fan, Wenchao Li +2
Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neu…