Evaluation of state representation methods in robot hand-eye coordination learning from demonstration
This work addresses the problem of selecting effective state representations for robot learning, which is incremental as it compares existing methods rather than introducing new ones.
The paper evaluated various state representation methods for robot hand-eye coordination learning from demonstration, focusing on their ability to reduce state dimensions, ensure controllability in different controllers, and transfer learned representations from humans to robots, with results visualized for comparison.
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.