Paper Info
Title
Towards Real-Time Physical Human-Robot Interaction Using Skeleton Information And Hand Gestures
Abstract
For successful physical human-robot interaction, the capability of a robot to understand its environment is imperative. More importantly, the robot should extract from the human operator as much information as possible. A reliable 3D skeleton extraction is essential for a robot to predict the intentions of the operator while s/he moves toward the robot or performs a meaningful gesture. For this purpose, we have integrated a time-of-flight depth camera with a state-of-the-art 2D skeleton extraction library namely Openpose, to obtain 3D skeletal joint coordinates reliably. We have also developed a robust and rotation invariant (in the coronal plane) hand gesture detector using a convolutional neural network. At run time (after having been trained) the detector does not require any pre-processing of the hand images. A complete pipeline for skeleton extraction and hand gesture recognition is developed and employed for real-time physical human-robot interaction, demonstrating the promising capability of the designed framework. This work establishes a firm basis and will be extended for the development of intelligent human intention detection in physical human-robot interaction scenarios, to efficiently recognize a variety of static as well as dynamic gestures.
Year
DOI
Venue
2018
10.1109/IROS.2018.8594385
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Computer vision,Computer science,Convolutional neural network,Gesture,Robot kinematics,Gesture recognition,Artificial intelligence,Operator (computer programming),Robot,Detector,Human–robot interaction
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Osama Mazhar100.68
Sofiane Ramdani2105.10
Benjamin Navarro394.59
Robin Passama4254.76
Andrea Cherubini518820.98