63 citations · 80 across the 38 of their papers we have counts for
6 papers · 1 filter
Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimization
Zahra Shahrooei, Mykel J. Kochenderfer, Ali Baheri
Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computat…
A Survey on Reinforcement Learning in Aviation Applications
Pouria Razzaghi, Amin Tabrizian, Wei Guo +6
Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-…
A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems
Ali Baheri, Hao Ren, Benjamin Johnson +2
We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems. Our proposed framework integrates two novel meth…
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes
Kyle Hayes, Michael W. Fouts, Ali Baheri +1
A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the…
A Framework for Controlling Multi-Robot Systems Using Bayesian Optimization and Linear Combination of Vectors
Stephen Jacobs, R. Michael Butts, Yu Gu +2
We propose a general framework for creating parameterized control schemes for decentralized multi-robot systems. A variety of tasks can be seen in the decentralized multi-robot lit…
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach
Jared J. Beard, Ali Baheri
The increasing use of autonomous and semi-autonomous agents in society has made it crucial to validate their safety. However, the complex scenarios in which they are used may make…