Galen Mullins

REDD-RQB

Publications

In this paper, we investigate the use of surrogate agents to accelerate test scenario generation for autonomous vehicles. Our goal is to train the surrogate to replicate the true performance modes of the system. We create these surrogates by utilizing imitation learning with deep neural networks. By using imitator surrogates in place of the true a   ...more

In this paper we propose a new method for generating test scenarios for black-box autonomous systems that demonstrate critical transitions in performance modes. This method provides a test engineer with key insights into the software’s decision-making engine and how those decisions affect transitions between performance modes. We achieve this via   ...more

To properly evaluate the ability of robots to operate autonomously in the real world, it is necessary to develop methods for quantifying their self-righting capabilities. Here, we improve upon a sampling-based framework for evaluating self-righting capabilities that was previously validated in two dimensions. To apply this framework to realistic r   ...more

We propose a novel method for generating test scenarios for a black box autonomous system that demonstrate critical transitions in its performance modes. In complex environments it is possible for an autonomous system to fail at its assigned mission even if it complies with requirements for all subsystems and throws no faults. This is particularly   ...more

Contact Us


Chief
Ashley Llorens
Ashley.Llorens@jhuapl.edu
240-228-0312

Physical Address
7701 Montpelier Road
Laurel, MD 20723


The Intelligent Systems Center is located at the Montpelier Campus of the Johns Hopkins Applied Physics Laboratory.
Click here for a map, directions and other visitor information.