  We evaluate different state representation methods in robot hand-eye
coordination learning on different aspects. Regarding state dimension
reduction: we evaluates how these state representation methods capture relevant
task information and how much compactness should a state representation be.
Regarding controllability: experiments are designed to use different state
representation methods in a traditional visual servoing controller and a
REINFORCE controller. We analyze the challenges arisen from the representation
itself other than from control algorithms. Regarding embodiment problem in LfD:
we evaluate different method's capability in transferring learned
representation from human to robot. Results are visualized for better
understanding and comparison.
