Name
Title | ||
|---|---|---|
Reconstructing State-Space from Movie Using Convolutional Autoencoder for Robot Control. |
Abstract | ||
|---|---|---|
In contrast with intensive studies for hardware development in soft robotics, approaches to construct a controller for soft robots has been relying on heuristics. One of the biggest reasons of this issue is that even reconstructing the state-space to describe the behavior is difficult. In this study, we propose a method to reconstruct state-space from movies using a convolutional autoencoder for robot control. In the proposed method, the process that reduces the number of dimensions of each frame in movies is regulated by additional losses making latent variables orthogonal each other and apt to model the forward dynamics. The proposed method was successfully validated through a simulation where a two links planar manipulator is modeled using the movie and controlled based on the forward model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01370-7_38 | IAS |
Field | DocType | Citations |
Computer vision,Robot control,Control theory,Autoencoder,Computer science,Soft robotics,Latent variable,Heuristics,Artificial intelligence,Robot,State space | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuma Takahara | 1 | 0 | 0.68 |
| Shuhei Ikemoto | 2 | 52 | 18.33 |
| Koh Hosoda | 3 | 395 | 67.84 |