Name
Title | ||
|---|---|---|
Slippage Detection Generalizing to Grasping of Unknown Objects Using Machine Learning With Novel Features. |
Abstract | ||
|---|---|---|
Real-time grasp stability is based on successful slippage detection. In this work, we consider slippage detection as a binary problem (slip, stable) and we propose a novel set of temporal and frequential features, extracted from force norm profiles and collected during reliable ground truth labeling processes, finally employed within the machine learning classification techniques. Classification p... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LRA.2018.2793346 | IEEE Robotics and Automation Letters |
Keywords | Field | DocType |
Feature extraction,Grasping,Force,Friction,Sensors,Time-frequency analysis | GRASP,Pattern recognition,Generalization,Control theory,Feature extraction,Slippage,Ground truth,Artificial intelligence,Engineering,Classifier (linguistics),Statistical classification,Binary number | Journal |
Volume | Issue | ISSN |
3 | 2 | 2377-3766 |
Citations | PageRank | References |
3 | 0.42 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Agriomallos | 1 | 3 | 0.76 |
| Stefanos Doltsinis | 2 | 30 | 4.59 |
| Ioanna Mitsioni | 3 | 3 | 0.42 |
| Zoe Doulgeri | 4 | 332 | 47.11 |