Online Fault Detection in Manipulation Tasks via Generative Models
Michael W. Lanighan,Oscar Youngquist,Michael W. Lanighan,Oscar Youngquist
This paper introduces a method, Generative Adversarial Networks for Detecting Erroneous Results (GANDER), leveraging Generative Adversarial Networks to provide online error detection in manipulation tasks for autonomous robot systems. GANDER relies on mapping input images of a trained task to a learned manifold that contains only positive task executions and outcomes. When reconstructed through th...
