215 citations · 275 across the 30 of their papers we have counts for
8 papers · 1 filter
Stealthy Perception-based Attacks on Unmanned Aerial Vehicles
Amir Khazraei, Haocheng Meng, Miroslav Pajic
In this work, we study vulnerability of unmanned aerial vehicles (UAVs) to stealthy attacks on perception-based control. To guide our analysis, we consider two specific missions: (…
AVstack: An Open-Source, Reconfigurable Platform for Autonomous Vehicle Development
R. Spencer Hallyburton, Shucheng Zhang, Miroslav Pajic
Pioneers of autonomous vehicles (AVs) promised to revolutionize the driving experience and driving safety. However, milestones in AVs have materialized slower than forecast. Two cu…
Reinforcement Learning with Temporal Logic Constraints for Partially-Observable Markov Decision Processes
Yu Wang, Alper Kamil Bozkurt, Miroslav Pajic
This paper proposes a reinforcement learning method for controller synthesis of autonomous systems in unknown and partially-observable environments with subjective time-dependent s…
Model-Free Learning of Safe yet Effective Controllers
Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
We study the problem of learning safe control policies that are also effective; i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task…
Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the…
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
Alper Kamil Bozkurt, Yu Wang, Michael Zavlanos +1
We study the problem of synthesizing control strategies for Linear Temporal Logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochasti…
Hyperproperties for Robotics: Planning via HyperLTL
Yu Wang, Siddhartha Nalluri, Miroslav Pajic
There is a growing interest on formal methods-based robotic planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-tempora…
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos +1
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that c…