Paper Info
Title
Proactive, incremental learning of gesture-action associations for human-robot collaboration
Abstract
Identifying an object of interest, grasping it, and handing it over are key capabilities of collaborative robots. In this context we propose a fast, supervised learning framework for learning associations between human hand gestures and the intended robotic manipulation actions. This framework enables the robot to learn associations on the fly while performing a task with the user. We consider a domestic scenario of assembling a kid's table where the role of the robot is to assist the user. To facilitate the collaboration we incorporate the robot's gaze into the framework. The proposed approach is evaluated in simulation as well as in a real environment. We study the effect of accurate gesture detection on the number of interactions required to complete the task. Moreover, our quantitative analysis shows how purposeful gaze can significantly reduce the amount of time required to achieve the goal.
Year
DOI
Venue
2017
10.1109/ROMAN.2017.8172325
2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
Keywords
Field
DocType
proactive learning,incremental learning,gesture-action associations,human-robot collaboration,collaborative robots,human hand gestures,domestic scenario,robot gaze,gesture detection,kid tabke assembling,robotic manipulation actions,supervised learning framework
Computer vision,Gaze,Computer science,Gesture,Gesture recognition,Supervised learning,Human–computer interaction,Artificial intelligence,Probabilistic logic,Robot,Human–robot interaction,Cognitive neuroscience of visual object recognition
Conference
ISSN
ISBN
Citations 
1944-9445
978-1-5386-3519-3
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
Dadhichi Shukla1213.11
Özgür Erkent2264.96
Justus H. Piater354361.56