Name
Title | ||
|---|---|---|
Using tactile sensation for learning contact knowledge: Discriminate collision from physical interaction |
Abstract | ||
|---|---|---|
Detecting and interpreting contacts is a crucial aspect of physical Human-Robot Interaction. In order to discriminate between intended and unintended contact types, we derive a set of linear and non-linear features based on physical contact model insights and from observing real impact data that may even rely on proprioceptive sensation only. We implement a classification system with a standard non-linear Support Vector Machine and show empirically both in simulations and on a real robot the high accuracy in off- as well as on-line settings of the system. We argue that these successful results are based on our feature design derived from first principles. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICRA.2015.7139726 | IEEE International Conference on Robotics and Automation |
Keywords | Field | DocType |
control engineering computing,human-robot interaction,learning systems,support vector machines,tactile sensors,SVM,classification system,contact knowledge learning,intended contact types,linear features,nonlinear features,physical contact model insights,physical human-robot interaction,proprioceptive sensation,standard nonlinear support vector machine,tactile sensation,unintended contact types | Computer vision,Physical interaction,Support vector machine,Feature extraction,Collision,Artificial intelligence,Engineering,Contact model,Robot,Feature design,Sensation | Conference |
Volume | Issue | ISSN |
2015 | 1 | 1050-4729 |
Citations | PageRank | References |
7 | 0.50 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saskia Golz | 1 | 7 | 0.50 |
| Christian Osendorfer | 2 | 125 | 13.24 |
| Sami Haddadin | 3 | 98 | 16.71 |