Name
Title | ||
|---|---|---|
Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation |
Abstract | ||
|---|---|---|
Interactions with an object during within-hand manipulation (WIHM) constitutes an assortment of gripping, sliding, and pivoting actions. In addition to manipulation benefits, the re-orientation and motion of the objects within-the-hand also provides a rich array of additional haptic information via the interactions to the sensory organs of the hand. In this article, we utilize variable friction (VF) robotic fingers to execute a rolling WIHM on a variety of objects, while recording `proprioceptive' actuator data, which is then used for object classification (i.e., without tactile sensors). Rather than hand-picking a select group of features for this task, our approach begins with 66 general features, which are computed from actuator position and load profiles for each object-rolling manipulation, based on gradient changes. An Extra Trees classifier performs object classification while also ranking each feature's importance. Using only the six most-important `Key Features' from the general set, a classification accuracy of 86% was achieved for distinguishing the six geometric objects included in our data set. Comparatively, when all 66 features are used, the accuracy is 89.8%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TOH.2019.2958669 | IEEE Transactions on Haptics |
Keywords | DocType | Volume |
Friction,Hand,Humans,Machine Learning,Motor Activity,Proprioception,Robotics,Touch Perception | Journal | 13 |
Issue | ISSN | Citations |
3 | 1939-1412 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Spiers | 1 | 70 | 8.99 |
| Andrew Morgan | 2 | 19 | 6.43 |
| Krishnan Srinivasan | 3 | 0 | 0.68 |
| Berk Çalli | 4 | 34 | 3.38 |
| Aaron M. Dollar | 5 | 1388 | 124.25 |