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Found 23 papers, 2 papers with code
Data-Driven Permissible Safe Control with Barrier Certificates
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates.
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning.
Shielded Deep Reinforcement Learning for Complex Spacecraft Tasking
Autonomous spacecraft control via Shielded Deep Reinforcement Learning (SDRL) has become a rapidly growing research area.
IntervalMDP.jl: Accelerated Value Iteration for Interval Markov Decision Processes
In this paper, we present IntervalMDP. jl, a Julia package for probabilistic analysis of interval Markov Decision Processes (IMDPs).
Recursively-Constrained Partially Observable Markov Decision Processes
Evaluations on a set of benchmark problems demonstrate the efficacy of our algorithm and show that policies for RC-POMDPs produce more desirable behaviors than policies for C-POMDPs.
Unifying Safety Approaches for Stochastic Systems: From Barrier Functions to Uncertain Abstractions via Dynamic Programming
Providing safety guarantees for stochastic dynamical systems has become a central problem in many fields, including control theory, machine learning, and robotics.
Formal Abstraction of General Stochastic Systems via Noise Partitioning
We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with non-standard (e. g., non-affine, non-symmetric, non-unimodal) noise distributions for verification purposes.
Promises of Deep Kernel Learning for Control Synthesis
Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes.
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks
Further, we show a method of computing the entire Pareto front (the set of all optimal trade-offs) via an adaptation of a multi-objective A* algorithm.
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs).
Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems
This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints.
Distributionally Robust Strategy Synthesis for Switched Stochastic Systems
The framework we present first learns an abstraction of a switched stochastic system as a robust Markov decision process (robust MDP) by accounting for both the stochasticity of the system and the uncertainty in the noise distribution.
Interval Markov Decision Processes with Continuous Action-Spaces
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals.
Pareto Optimal Strategies for Event-Triggered Estimation
A well understood technique for trading off communication costs with estimation accuracy is event triggering (ET), where measurements are only communicated when useful, e. g., when Kalman filter innovations exceed some threshold.
Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications
This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications.
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
A key step in our method is the employment of the recent convex approximation results for NNs to find piece-wise linear bounds, which allow the formulation of the barrier function synthesis problem as a sum-of-squares optimization program.
Formal Control Synthesis for Stochastic Neural Network Dynamic Models
Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control systems with complicated physics or black-box components.
Conflict-Based Search for Explainable Multi-Agent Path Finding
In the Multi-Agent Path Finding (MAPF) problem, the goal is to find non-colliding paths for agents in an environment, such that each agent reaches its goal from its initial location.
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression
In this article, we develop a framework for verifying partially-observable, discrete-time dynamical systems with unmodelled dynamics against temporal logic specifications from a given input-output dataset.
Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
The online controller may improve the baseline guarantees since it avoids the discretization error and reduces regression error as new data is collected.
Strategy Synthesis for Partially-known Switched Stochastic Systems
We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems.
LTLf Synthesis on Probabilistic Systems
Linear Temporal Logic over finite traces (LTLf) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties.
Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments
The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment, and (ii) iteratively (re)planning trajectories to goal that are kinodynamically feasible and probabilistically safe through a multi-layered sampling-based planner in the belief space.
