Search Results for author:
Found 19 papers, 3 papers with code
Distributionally Time-Varying Online Stochastic Optimization under Polyak-Łojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning
In this work, we consider a sequence of stochastic optimization problems following a time-varying distribution via the lens of online optimization.
Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking: Extended Version
We adopted the notion of dynamic regret to measure the performance of this proposed online LQR control method, with our main result being that the (dynamic) regret of our method is upper bounded by a constant.
Minimum-Time Escape from a Circular Region for a Dubins Car
We investigate the problem of finding paths that enable a robot modeled as a Dubins car (i. e., a constant-speed finite-turn-rate unicycle) to escape from a circular region of space in minimum time.
The Replicator Dynamic, Chain Components and the Response Graph
We settle an open problem establishing that under the replicator, sink chain components -- a topological notion of long-run outcome of a dynamical system -- always exist and are approximated by the sink connected components of the game's response graph.
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design.
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.
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity.
Learning Safety Filters for Unknown Discrete-Time Linear Systems
A learning-based safety filter is developed for discrete-time linear time-invariant systems with unknown models subject to Gaussian noises with unknown covariance.
Toward nonlinear dynamic control over encrypted data for infinite time horizon
In this note, we demonstrate that it is possible to run a dynamic controller over encrypted data for an infinite time horizon if the output of the controller can be represented as a function of a fixed number of previous inputs and outputs.
Temporal Logic Planning for Minimum-Time Positioning of Multiple Threat-Seduction Decoys
The task of a decoy reaching a state in which the lock of the assigned threat can be broken is formulated as a temporal logic specification.
Fast Spline Trajectory Planning: Minimum Snap and Beyond
In this paper, we study spline trajectory generation via the solution of two optimisation problems: (i) a quadratic program (QP) with linear equality constraints and (ii) a nonlinear and nonconvex optimisation program.
An expressiveness hierarchy of Behavior Trees and related architectures
In this paper we provide a formal framework for comparing the expressive power of Behavior Trees (BTs) to other action selection architectures.
Safe Learning of Uncertain Environments
We show that with an arbitrarily large probability we can guarantee that the state will remain in the safe set, while learning and control are carried out simultaneously, provided that a feasible solution exists for the optimization problem.
Approximate Midpoint Policy Iteration for Linear Quadratic Control
We present a midpoint policy iteration algorithm to solve linear quadratic optimal control problems in both model-based and model-free settings.
On modularity in reactive control architectures, with an application to formal verification
We use a Linear Temporal Logic-based verification scheme to verify the correctness of this structure, and then show how one can modify modules while preserving its correctness.
A principled analysis of Behavior Trees and their generalisations
As complex autonomous robotic systems become more widespread, the need for transparent and reusable Artificial Intelligence (AI) designs becomes more apparent.
Online Stochastic Convex Optimization: Wasserstein Distance Variation
Distributionally-robust optimization is often studied for a fixed set of distributions rather than time-varying distributions that can drift significantly over time (which is, for instance, the case in finance and sociology due to underlying expansion of economy and evolution of demographics).
Ordinal Optimisation for the Gaussian Copula Model
We present results on the estimation and evaluation of success probabilities for ordinal optimisation over uncountable sets (such as subsets of $\mathbb{R}^{d}$).
Optimization and Control Probability
Predictive Online Convex Optimization
We show that under these conditions and without any assumptions on the predictability of the environment, the predictive update strictly improves on the performance of the standard update.
