Search Results for author:
Found 14 papers, 3 papers with code
Scaling Learning based Policy Optimization for Temporal Tasks via Dropout
We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives.
Robust Conformal Prediction for STL Runtime Verification under Distribution Shift
To address these challenges, we assume to know an upper bound on the statistical distance (in terms of an f-divergence) between the distributions at deployment and design time, and we utilize techniques based on robust conformal prediction.
Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions
We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems.
Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives
In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints while optimizing certain (soft) performance objectives.
Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic
Automated vehicles (AV) heavily depend on robust perception systems.
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions
Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value.
Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees
Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems.
Local Repair of Neural Networks Using Optimization
In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties.
Search-based Test-Case Generation by Monitoring Responsibility Safety Rules
The safety of Automated Vehicles (AV) as Cyber-Physical Systems (CPS) depends on the safety of their consisting modules (software and hardware) and their rigorous integration.
DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems
The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles.
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems.
Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components
Additionally, we present three driving scenarios and demonstrate how our requirements-driven testing framework can be used to identify critical system behaviors, which can be used to support the development process.
Gray-box Adversarial Testing for Control Systems with Machine Learning Component
Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics.
Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components
We present a testing framework that is compatible with test case generation and automatic falsification methods, which are used to evaluate cyber-physical systems.
