Effect of Optimizer, Initializer, and Architecture of Hypernetworks on
  Continual Learning from Demonstration
Sayantan Auddy Sebastian Bergner Justus Piater
In continual learning from demonstration (CLfD), a robot learns a sequence of
real-world motion skills continually from human demonstrations. Recently,
hypernetworks have been successful in solving this problem. In this paper, we
perform an exploratory study of the effects of different optimizers,
initializers, and network architectures on the continual learning performance
of hypernetworks for CLfD. Our results show that adaptive learning rate
optimizers work well, but initializers specially designed for hypernetworks
offer no advantages for CLfD. We also show that hypernetworks that are capable
of stable trajectory predictions are robust to different network architectures.
Our open-source code is available at
https://github.com/sebastianbergner/ExploringCLFD.