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Found 24 papers, 8 papers with code
Data-Driven Reachability Analysis of Stochastic Dynamical Systems with Conformal Inference
We consider data-driven reachability analysis of discrete-time stochastic dynamical systems using conformal inference.
Conformance Testing for Stochastic Cyber-Physical Systems
Ultimately, conformance can capture distance between design models and their real implementations and thus aid in robust system design.
Efficient STL Control Synthesis under Asynchronous Temporal Robustness Constraints
Given an ATR bound, we compute a sequence of control inputs so that the specification is satisfied by the system as long as each sub-trajectory is shifted not more than the ATR bound.
Control of Nonlinear Systems Under Multiple Time-Varying Output Constraints: A Single Funnel Approach
This paper proposes a novel control framework for handling (potentially coupled) multiple time-varying output constraints for uncertain nonlinear systems.
Combined Left and Right Temporal Robustness for Control under STL Specifications
Many modern autonomous systems, particularly multi-agent systems, are time-critical and need to be robust against timing uncertainties.
Conformal Prediction Regions for Time Series using Linear Complementarity Programming
In fact, to obtain prediction regions over $T$ time steps with confidence $1-\delta$, {previous works require that each individual prediction region is valid} with confidence $1-\delta/T$.
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction
However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior.
Physics Constrained Motion Prediction with Uncertainty Quantification
Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems.
Conformal Prediction for STL Runtime Verification
The second algorithm constructs prediction regions for future system states first, and uses these to obtain a prediction region for the satisfaction measure.
Risk Verification of Stochastic Systems with Neural Network Controllers
Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we present a data-driven framework for verifying the risk of stochastic dynamical systems with NN controllers.
Chordal Sparsity for SDP-based Neural Network Verification
Neural networks are central to many emerging technologies, but verifying their correctness remains a major challenge.
Risk of Stochastic Systems for Temporal Logic Specifications
For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly.
Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic Systems
Our control approach relies on reformulating these risk predicates as deterministic predicates over mean and covariance states of the system.
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data.
Temporal Robustness of Temporal Logic Specifications: Analysis and Control Design
We study the temporal robustness of temporal logic specifications and show how to design temporally robust control laws for time-critical control systems.
Temporal Robustness of Stochastic Signals
We then define the temporal robustness risk by investigating the temporal robustness of the realizations of a stochastic signal.
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
We then present an optimization problem to learn ROCBFs from expert demonstrations that exhibit safe system behavior, e. g., data collected from a human operator.
Reactive and Risk-Aware Control for Signal Temporal Logic
Addressing these is pivotal to build fully autonomous systems and requires a systematic integration of planning and control.
Time-Robust Control for STL Specifications
We present a robust control framework for time-critical systems in which satisfying real-time constraints robustly is of utmost importance for the safety of the system.
STL Robustness Risk over Discrete-Time Stochastic Processes
We then define the risk of a stochastic process not satisfying an STL formula robustly, referred to as the STL robustness risk.
Barrier Function-based Collaborative Control of Multiple Robots under Signal Temporal Logic Tasks
Motivated by the recent interest in cyber-physical and autonomous robotic systems, we study the problem of dynamically coupled multi-agent systems under a set of signal temporal logic tasks.
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions.
Learning Hybrid Control Barrier Functions from Data
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data.
Learning Control Barrier Functions from Expert Demonstrations
Furthermore, if the CBF parameterization is convex, then under mild assumptions, so is our learning process.
