Search Results for author:
Found 10 papers, 0 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.
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.
Convex Optimization-based Policy Adaptation to Compensate for Distributional Shifts
The problem addressed by this paper is the following: Suppose we obtain an optimal trajectory by solving a control problem in the training environment, how do we ensure that the real-world system trajectory tracks this optimal trajectory with minimal amount of error in a deployment environment.
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems
In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation.
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.
Performance Bounds for Neural Network Estimators: Applications in Fault Detection
We exploit recent results in quantifying the robustness of neural networks to input variations to construct and tune a model-based anomaly detector, where the data-driven estimator model is provided by an autoregressive neural network.
Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization
Abstracting neural networks with constraints they impose on their inputs and outputs can be very useful in the analysis of neural network classifiers and to derive optimization-based algorithms for certification of stability and robustness of feedback systems involving neural networks.
Generalized Outer Bounds on the Finite Geometric Sum of Ellipsoids
General results on convex bodies are reviewed and used to derive an exact closed-form parametric formula for the boundary of the geometric (Minkowski) sum of $k$ ellipsoids in $n$-dimensional Euclidean space.
Vibration transfer path analysis and path ranking for NVH optimization of a vehicle interior
The procedure shows a good ability of vibration path ranking in a vehicle and is an effective tool to diagnose the vibration problem inside the vehicle.
