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arXiv · 1903.00634

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

Jun Jin·Masood Dehghan·Laura Petrich·Steven Weikai Lu·Martin Jagersand
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Abstract

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.

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Jun Jin, Masood Dehghan, Laura Petrich, Steven Weikai Lu, Martin Jagersand. 2019-03-02. Evaluation of state representation methods in robot hand-eye coordination learning from demonstration. https://arxiv.org/abs/1903.00634

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