7 papers
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…
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…
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…
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…
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…