  Data driven control of a continuum manipulator requires a lot of data for
training but generating sufficient amount of real time data is not cost
efficient. Random actuation of the manipulator can also be unsafe sometimes.
Meta learning has been used successfully to adapt to a new environment. Hence,
this paper tries to solve the above mentioned problem using meta learning. We
consider two cases for that. First, this paper proposes a method to use
simulation data for training the model using MAML(Model-Agnostic
Meta-Learning). Then, it adapts to the real world using gradient steps.
Secondly,if the simulation model is not available or difficult to formulate,
then we propose a CGAN(Conditional Generative adversial network)-MAML based
method for it. The model is trained using a small amount of real time data and
augmented data for different loading conditions. Then, adaptation is done in
the real environment. It has been found out from the experiments that the
relative positioning error for both the cases are below 3%. The proposed models
are experimentally verified on a real continuum manipulator.
