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.
@article{arxiv.1903.00634,
title = {Evaluation of state representation methods in robot hand-eye coordination learning from demonstration},
author = {Jun Jin and Masood Dehghan and Laura Petrich and Steven Weikai Lu and Martin Jagersand},
journal= {arXiv preprint arXiv:1903.00634},
year = {2019}
}