13 papers
CrossSafe: Towards Cross-Embodiment Latent Safety Filters
Ihab Tabbara, Yuxuan Yang, Hussein Sibai
Cross-embodiment learning has shown that a single model, such as a vision-language-action (VLA) model, can learn state representations and manipulation skills that can be applied a…
Towards General Language-Conditioned Latent Safety Filters
Ihab Tabbara, Yuxuan Yang, Hussein Sibai
Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language. Safe dep…
On the Limits of Sampling-Based Reachability: Geometry, Dynamics, and Sample Complexity
Jixian Liu, Ihab Tabbara, Hussein Sibai +1
Reachability analysis is central to safety-critical control, robotics, and neural network verification, but classical computational methods, such as Hamilton--Jacobi reachability a…
Safe Control using Learned Safety Filters and Adaptive Conformal Inference
Sacha Huriot, Ihab Tabbara, Hussein Sibai
Safety filters have been shown to be effective tools to ensure the safety of control systems with unsafe nominal policies. To address scalability challenges in traditional synthesi…
Statistically Assuring Safety of Control Systems using Ensembles of Safety Filters and Conformal Prediction
Ihab Tabbara, Yuxuan Yang, Hussein Sibai
Safety assurance is a fundamental requirement for deploying learning-enabled autonomous systems. Hamilton-Jacobi (HJ) reachability analysis is a fundamental method for formally ver…
Computing Sound Lower and Upper Bounds on Hamilton-Jacobi Reach-Avoid Value Functions
Ihab Tabbara, Eliya Badr, Hussein Sibai
Hamilton-Jacobi (HJ) reachability analysis is a fundamental tool for the safety verification and control synthesis of nonlinear control systems. Classical HJ reachability analysis…
Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning
Yuxuan Yang, Hussein Sibai
Safety is a fundamental requirement for autonomous systems operating in critical domains. Control barrier functions (CBFs) have been used to design safety filters that minimally al…
Designing Latent Safety Filters using Pre-Trained Vision Models
Ihab Tabbara, Yuxuan Yang, Ahmad Hamzeh +2
Ensuring safety of vision-based control systems remains a major challenge hindering their deployment in critical settings. Safety filters have gained increased interest as effectiv…
Learning Conservative Neural Control Barrier Functions from Offline Data
Ihab Tabbara, Hussein Sibai
Safety filters, particularly those based on control barrier functions, have gained increased interest as effective tools for safe control of dynamical systems. Existing correct-by-…
Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees
Xinhang Ma, Junlin Wu, Hussein Sibai +2
Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the fact that the relationship betwe…
Learning Ensembles of Vision-based Safety Control Filters
Ihab Tabbara, Hussein Sibai
Safety filters in control systems correct nominal controls that violate safety constraints. Designing such filters as functions of visual observations in uncertain and complex envi…
Pre-Trained Vision Models as Perception Backbones for Safety Filters in Autonomous Driving
Yuxuan Yang, Hussein Sibai
End-to-end vision-based autonomous driving has achieved impressive success, but safety remains a major concern. The safe control problem has been addressed in low-dimensional setti…
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks
Junlin Wu, Hussein Sibai, Yevgeniy Vorobeychik
Function approximation has enabled remarkable advances in applying reinforcement learning (RL) techniques in environments with high-dimensional inputs, such as images, in an end-to…