# Evaluation of state representation methods in robot hand-eye coordination learning from demonstration

arXiv: https://arxiv.org/abs/1903.00634 · HTML: https://mlsanity.com/paper/1903.00634/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [A Geometric Perspective on Visual Imitation Learning](https://mlsanity.com/paper/2003.02768/) (2020)

## Abstract (arXiv)

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.