Search Results for author: John S. Baras

Found 17 papers, 0 papers with code

Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling

no code implementations16 Aug 2024 Clinton Enwerem, Erfaun Noorani, John S. Baras, Brian M. Sadler

With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes.

Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis

no code implementations13 Mar 2024 Rui Liu, Erfaun Noorani, Pratap Tokekar, John S. Baras

In this study, we conduct a thorough iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm and employing the exponential utility function.

Reinforcement Learning (RL)

Safe Collective Control under Noisy Inputs and Competing Constraints via Non-Smooth Barrier Functions

no code implementations6 Nov 2023 Clinton Enwerem, John S. Baras

We consider the problem of safely coordinating ensembles of identical autonomous agents to conduct complex missions with conflicting safety requirements and under noisy control inputs.

Model Predictive Control Stochastic Optimization

Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning

no code implementations2 Oct 2023 Armin Lederer, Erfaun Noorani, John S. Baras, Sandra Hirche

We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success.

reinforcement-learning Reinforcement Learning +1

Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent Systems

no code implementations17 Sep 2023 Clinton Enwerem, John S. Baras

To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems.

PASA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem

no code implementations31 Aug 2023 Nariman Torkzaban, Anousheh Gholami, John S. Baras, Bruce Golden

While the proposed a priori splitting rule in Chen et al. (2017) is fixed for all customers regardless of their demand and location, we suggest an adaptive splitting rule that takes into account the distance of the customers to the depot and their demand values.

Learning Agent Interactions from Density Evolution in 3D Regions With Obstacles

no code implementations18 May 2023 Amoolya Tirumalai, Christos N. Mavridis, John S. Baras

In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles.

Mobile Network Slicing under Demand Uncertainty: A Stochastic Programming Approach

no code implementations27 Apr 2023 Anousheh Gholami, Nariman Torkzaban, John S. Baras

At each microscale instance, utilizing the exact slice demand profiles, a linear program is solved to jointly minimize the unsupported traffic and the resource cost at the RAN.

Capacitated Beam Placement for Multi-beam Non-Geostationary Satellite Systems

no code implementations18 Jan 2023 Nariman Torkzaban, Asim Zoulkarni, Anousheh Gholami, John S. Baras

Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention both from industry and academia, over the past several years.

Sensor Scheduling for Linear Systems: A Covariance Tracking Approach

no code implementations17 Oct 2021 Dipankar Maity, David Hartman, John S. Baras

We propose a convex relaxation to the sensor design problem and a reference covariance trajectory is obtained from solving the relaxed sensor design problem.

Scheduling

On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective

no code implementations16 Apr 2021 Anousheh Gholami, Nariman Torkzaban, John S. Baras

In the first example, we show how utilizing the trust evidence can improve the performance and the security of Federated Learning.

Decision Making Federated Learning +1

Value of information in networked control systems subject to delay

no code implementations7 Apr 2021 Siyi Wang, Qingchen Liu, Precious Ugo Abara, John S. Baras, Sandra Hirche

In this paper, we study the trade-off between the transmission cost and the control performance of the multi-loop networked control system subject to network-induced delay.

Scheduling

Joint Satellite Gateway Deployment & Controller Placement in Software-Defined 5G-Satellite Integrated Networks

no code implementations15 Mar 2021 Nariman Torkzaban, John S. Baras

Several challenging optimization problems arise while considering the deployment of the space-air-ground integrated networks (SAGINs), among which the optimal satellite gateway deployment problem is of significant importance.

Cooperative Hypothesis Testing by Two Observers with Asymmetric Information

no code implementations25 Mar 2020 Aneesh Raghavan, John S. Baras

There are two problems to be solved by the observers: (i) true state of nature is known: find the distribution of the local information collected; (ii) true state of nature is unknown: collaboratively estimate the same using the distributions found by solving the first problem.

Vocal Bursts Valence Prediction

Co-active Learning to Adapt Humanoid Movement for Manipulation

no code implementations12 Sep 2016 Ren Mao, John S. Baras, Yezhou Yang, Cornelia Fermuller

It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with various constraints.

Active Learning Motion Generation

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