Name
Title | ||
|---|---|---|
Evaluation of state representation methods in robot hand-eye coordination learning from demonstration. |
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 methodu0027s capability in transferring learned representation from human to robot. Results are visualized for better understanding and comparison. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Robotics | Journal |
Volume | Citations | PageRank |
abs/1903.00634 | 1 | 0.35 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Jin | 1 | 1 | 1.03 |
| Masood Dehghan | 2 | 49 | 7.11 |
| Laura Petrich | 3 | 8 | 2.83 |
| Steven Weikai Lu | 4 | 1 | 0.35 |
| Martin Jägersand | 5 | 334 | 43.10 |