no code implementations • 15 Mar 2024 • Nhan-Khanh Le, Erfaun Noorani, Sandra Hirche, John Baras
We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in Signal Temporal Logic (STL).
no code implementations • 14 Jan 2023 • Clinton Enwerem, John Baras, Danilo Romero
In this paper, we present a distributed optimal multiagent control scheme for quadrotor formation tracking under localization errors.
no code implementations • 18 Dec 2022 • Erfaun Noorani, Christos Mavridis, John Baras
While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variance in the total reward amongst different episodes in slightly different environments.
1 code implementation • 15 Dec 2022 • Christos Mavridis, John Baras
This simulates an annealing process and defines a robust and interpretable heuristic method to gradually increase the complexity of the learning architecture in a task-agnostic manner, giving emphasis to regions of the data space that are considered more important according to a predefined criterion.
1 code implementation • 6 Sep 2022 • Christos Mavridis, John Baras
We develop an online prototype-based learning algorithm based on annealing optimization that is formulated as an online gradient-free stochastic approximation algorithm.
no code implementations • 5 Dec 2021 • Christos Mavridis, Amoolya Tirumalai, John Baras
We describe the dynamics of the swarm based on pairwise interactions according to a Cucker-Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations.
1 code implementation • 4 Dec 2021 • Christos Mavridis, John Baras
We approach the design of such a learning architecture from a system-theoretic viewpoint, developing a closed-loop system with three main components: (i) a multi-resolution analysis pre-processor, (ii) a group-invariant feature extractor, and (iii) a progressive knowledge-based learning module.
1 code implementation • 11 Feb 2021 • Christos Mavridis, John Baras
Inherent in virtually every iterative machine learning algorithm is the problem of hyper-parameter tuning, which includes three major design parameters: (a) the complexity of the model, e. g., the number of neurons in a neural network, (b) the initial conditions, which heavily affect the behavior of the algorithm, and (c) the dissimilarity measure used to quantify its performance.
no code implementations • 27 Mar 2020 • Erfaun Noorani, Yagiz Savas, Alec Koppel, John Baras, Ufuk Topcu, Brian M. Sadler
In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold.
no code implementations • 12 Sep 2016 • Wentao Luan, Yezhou Yang, Cornelia Fermuller, John Baras
We consider the problem of object recognition in 3D using an ensemble of attribute-based classifiers.