Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-end Robot Learning Scheme
Mathias Lechner,Ramin Hasani,Daniela Rus,Radu Grosu,Mathias Lechner,Ramin Hasani,Daniela Rus,Radu Grosu
Traditional robotic control suits require profound task-specific knowledge for designing, building and testing control software. The rise of Deep Learning has enabled end-to-end solutions to be learned entirely from data, requiring minimal knowledge about the application area. We design a learning scheme to train end-to-end linear dynamical systems (LDS)s by gradient descent in imitation learning ...
