Paper Info
Title
Intention And Engagement Recognition For Personalized Human-Robot Interaction, An Integrated And Deep Learning Approach
Abstract
The quality of the interaction between two individuals depends upon not only exchange (i.e. understanding partner's intention and reacting to it), but also on how personalized is the interaction. In this work, we have set out to accomplish these objectives for Human Robot Interaction. For this, we have developed a distributed and multimodal data acquisition and interaction manager architecture aiming to enable personalized Human-Robot Interactions. In the proposed approach, high-level perceptual capabilities (i.e. recognizing human activity and engagement) are performed by an Autoencoder, which is a Deep Learning and Unsupervised Learning method. This Autoencoder module is integrated with a facial recognition and a dialog manager (speech recognition and speech generation) to enable personalized interaction. We discuss the advantages of Autoencoders over Supervised Learning methods, and how our proposed architecture can be used to increase the duration of interaction with a robot during unscripted scenarios. Experimental validations are also performed in real Human-Robot interactions using a humanoid robot.
Year
DOI
Venue
2019
10.1109/ICARM.2019.8834226
2019 IEEE 4TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2019)
Keywords
Field
DocType
Personalized Human Robot Interaction, Intention and engagement recognition, Deep learning, Autoencoder, Intelligent and autonomous robots
Facial recognition system,Autoencoder,Computer science,Supervised learning,Human–computer interaction,Unsupervised learning,Artificial intelligence,Deep learning,Robot,Human–robot interaction,Humanoid robot
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Suraj Prakash Pattar100.34
Enrique Coronado2185.42
Liz Katherine Rincon Ardila300.34
Gentiane Venture420938.49