Generative Adversarial Networks for Detecting Erroneous Results

As part of the broader ARTEMIS effort, NASA#39;s Human Exploration and Operations Mission Directorate (HEOMD) is targeting an increase in the use of robot and automated systems to enable the unattended setup, operation, and maintenance of ground systems and systems on the surfaces of other planets and moons. There is a critical need for technology to realize this target, specifically technologies to enable autonomous inspection, maintenance, and repair (IMamp;R).nbsp;Under previously funded NASA efforts TRACLabs has developed CRAFTSMAN, a supervisorynbsp;robot control framework. However, thisnbsp;approachnbsp;still requires supervisory/operator interaction to perform verification of task outcomes and inspection. This potentially limits the ability to widely deploy such a supervisory system due to the level of required operator attention and interaction. Techniques to automate such tasks are needed to reduce operator burden. Additionally, the lack of robust error detection becomes increasingly critical in remote tasks on the lunar surface and in dangerous ground-based tasks such as those involving propellant transfer. To address this shortcoming and to increase the autonomous capabilities of TRACLabs#39; robot control suite for use in HEOMD domains TRACLabs proposes the Generative Adversarial Networks for Detecting Erroneous Results (GANDER) system to leverage Generative Adversarial Networks to perform online error detection in ground operations tasks. The resulting system will increase the inspection and task outcome verification capabilities of these systems, thus increasing the autonomous behavior of deployed robot systems on Earth and on other planets and moons.nbsp;