2 citations · 3 across the 13 of their papers we have counts for
13 papers
Coordinated UAV Beamforming and Control for Directional Jamming and Nulling
Filippos Fotiadis, Brian M. Sadler, Ufuk Topcu
Efficient mobile jamming against eavesdroppers in wireless networks necessitates accurate coordination between mobility and antenna beamforming. We study the coordinated beamformin…
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks
Wesley A Suttle, Vipul K Sharma, Brian M Sadler
Multi-agent reinforcement learning (MARL) methods typically require that agents enjoy global state observability, preventing development of decentralized algorithms and limiting sc…
Optimal Satellite Maneuvers for Spaceborne Jamming Attacks
Filippos Fotiadis, Quentin Rommel, Brian M. Sadler +1
Satellites are becoming exceedingly critical for communication, making them prime targets for cyber-physical attacks. We consider a rogue satellite in low Earth orbit that jams the…
Value of Information-based Deceptive Path Planning Under Adversarial Interventions
Wesley A. Suttle, Jesse Milzman, Mustafa O. Karabag +2
Existing methods for deceptive path planning (DPP) address the problem of designing paths that conceal their true goal from a passive, external observer. Such methods do not apply…
Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach
Utsav Singh, Souradip Chakraborty, Wesley A. Suttle +6
Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundament…
Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling
Clinton Enwerem, Erfaun Noorani, John S. Baras +1
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 algori…
DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning
Utsav Singh, Souradip Chakraborty, Wesley A. Suttle +3
Learning control policies to perform complex robotics tasks from human preference data presents significant challenges. On the one hand, the complexity of such tasks typically requ…
Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem
Anurag Gupta, Vikram Krishnamurthy, Brian M. Sadler
This paper introduces a novel non-parametric change detection algorithm to identify eclipse attacks on a blockchain network; the non-parametric algorithm relies only on the empiric…
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
Utsav Singh, Wesley A. Suttle, Brian M. Sadler +2
In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based…
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju +4
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of co…
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A. Suttle, Alec Koppel +4
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy need…
Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler
Deceptive path planning (DPP) is the problem of designing a path that hides its true goal from an outside observer. Existing methods for DPP rely on unrealistic assumptions, such a…
On the Vulnerability of LLM/VLM-Controlled Robotics
Xiyang Wu, Souradip Chakraborty, Ruiqi Xian +6
In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities. While…