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Found 22 papers, 2 papers with code
Conformal Predictive Programming for Chance Constrained Optimization
Motivated by the advances in conformal prediction (CP), we propose conformal predictive programming (CPP), an approach to solve chance constrained optimization (CCO) problems, i. e., optimization problems with nonlinear constraint functions affected by arbitrary random parameters.
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.
Signal Temporal Logic-Guided Apprenticeship Learning
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations.
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.
Multi Agent Path Finding using Evolutionary Game Theory
In this paper, we consider the problem of path finding for a set of homogeneous and autonomous agents navigating a previously unknown stochastic environment.
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-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.
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic
In this work, we propose DIALOGUESTL, an interactive approach for learning correct and concise STL formulas from (often) ambiguous NL descriptions.
Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic
Automated vehicles (AV) heavily depend on robust perception systems.
Learning Performance Graphs from Demonstrations via Task-Based Evaluations
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots.
Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives
There are several examples of the use of formal languages such as temporal logics and automata to specify high-level task specifications for robots (in lieu of Markovian rewards).
Non-Markovian Reinforcement Learning using Fractional Dynamics
Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment.
Learning from Demonstrations using Signal Temporal Logic
Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions.
Model-based Reinforcement Learning from Signal Temporal Logic Specifications
In this paper, we propose expressing desired high-level robot behavior using a formal specification language known as Signal Temporal Logic (STL) as an alternative to reward/cost functions.
DiffRNN: Differential Verification of Recurrent Neural Networks
As the memory footprint and energy consumption of such components become a bottleneck, there is interest in compressing and optimizing such networks using a range of heuristic techniques.
Mining Environment Assumptions for Cyber-Physical System Models
We assume that the correctness of each component can be specified as a requirement satisfied by the output signals produced by the component, and that such an output guarantee is expressed in a real-time temporal logic such as Signal Temporal Logic (STL).
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data.
Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems
We present a simulation-based approach for generating barrier certificate functions for safety verification of cyber-physical systems (CPS) that contain neural network-based controllers.
Time Series Learning using Monotonic Logical Properties
Cyber-physical systems of today are generating large volumes of time-series data.
Logic-based Clustering and Learning for Time-Series Data
To effectively analyze and design cyberphysical systems (CPS), designers today have to combat the data deluge problem, i. e., the burden of processing intractably large amounts of data produced by complex models and experiments.
