Name
Abstract | ||
|---|---|---|
A physical Human-Robot Interaction (pHRI) framework is proposed using vision and force sensors for a two-way object hand-over task. Kinect v2 is integrated with the state-of-the-art 2D skeleton extraction library namely Openpose to obtain a 3D skeleton of the human operator. A robust and rotation invariant (in the coronal plane) hand gesture recognition system is developed by exploiting a convolutional neural network. This network is trained such that the gestures can be recognized without the need to pre-process the RGB hand images at run time. This work establishes a firm basis for the robot control using hand-gestures. This will be extended for the development of intelligent human intention detection in pHRI scenarios to efficiently recognize a variety of static as well as dynamic gestures. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ARSO.2018.8625753 | ARSO |
Keywords | Field | DocType |
Robot kinematics,Skeleton,Robot sensing systems,Human-robot interaction,Three-dimensional displays,Real-time systems | Computer vision,Robot control,Gesture,Computer science,Convolutional neural network,Robot kinematics,Gesture recognition,Artificial intelligence,Invariant (mathematics),RGB color model,Human–robot interaction | Conference |
ISSN | ISBN | Citations |
2162-7568 | 978-1-5386-8037-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osama Mazhar | 1 | 0 | 0.68 |
| Sofiane Ramdani | 2 | 10 | 5.10 |
| Benjamin Navarro | 3 | 9 | 4.59 |
| Robin Passama | 4 | 25 | 4.76 |