Name
Abstract | ||
|---|---|---|
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an unsupervised learning algorithm that transforms the complex tactile data into a compact, latent representation without the need to record ground truth reference data. These compact representations can either be used directly in a reinforcement learning based controller or can be used to calibrate the tactile sensor to physical quantities with only a few datapoints. We show the quality of our latent representation by predicting important features and with a simple control task. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Robotics | Reference data (financial markets),Computer vision,Unsupervised learning algorithm,Control theory,GRASP,Pattern recognition,Computer science,Ground truth,Preprocessor,Artificial intelligence,Reinforcement learning,Tactile sensor |
DocType | Volume | Citations |
Journal | abs/1606.07312 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maximilian Karl | 1 | 9 | 2.40 |
| Justin Bayer | 2 | 157 | 32.38 |
| Patrick van der Smagt | 3 | 188 | 24.23 |