8 papers
Enhancing Certifiable Semantic Robustness via Robust Pruning of Deep Neural Networks
Hanjiang Hu, Bowei Li, Ziwei Wang +4
Deep neural networks have been widely adopted in many vision and robotics applications with visual inputs. It is essential to verify its robustness against semantic transformation…
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Hanjiang Hu, Luca Marzari +4
Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across diverse applications, ranging from image classification to control. Verifying specific input-outp…
Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
Yifan Sun, Feihan Li, Weiye Zhao +3
Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability. In particular, explorations during lear…
Bridging the Gap between Hardware Fuzzing and Industrial Verification
Ruiyang Ma, Tianhao Wei, Jiaxi Zhang +3
As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can…
Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control
Xiao Li, Tianhao Wei, Changliu Liu +2
Neural networks are powerful tools for data-driven modeling of complex dynamical systems, enhancing predictive capability for control applications. However, their inherent nonlinea…
Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills
Tianhao Wei, Liqian Ma, Rui Chen +2
The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks require precise motion while others require force compliance; some tasks require av…