“Let There Be Intelligence!”- A Novel Cognitive Architecture for Teaching Assistant Social Robots

This paper addresses the lack of proper Learning from Demonstration (LfD) architectures for Sign Language-based Human–Robot Interactions to make them more extensible. The paper proposes and implements a Learning from Demonstration structure for teaching new Iranian Sign Language signs to a teacher assistant social robot, RASA. This LfD architecture utilizes one-shot learning techniques and Convolutional Neural Network to learn to recognize and imitate a sign after seeing its demonstration (using a data glove) just once. Despite using a small, low diversity data set (~ 500 signs in 16 categories), the recognition module reached a promising 4-way accuracy of 70% on the test data and showed good potential for increasing the extensibility of sign vocabulary in sign language-based human–robot interactions. The expansibility and promising results of the one-shot Learning from Demonstration technique in this study are the main achievements of conducting such machine learning algorithms in social Human–Robot Interaction.

show abstract

“Let There Be Intelligence!”- A Novel Cognitive Architecture for Teaching Assistant Social Robots

Cited by 5 publications

References 15 publications

Teaching Persian Sign Language to a Social Robot via the Learning from Demonstrations Approach

Teaching Persian Sign Language to a Social Robot via the Learning from Demonstrations Approach

Automated Lip-Reading Robotic System Based on Convolutional Neural Network and Long Short-Term Memory

One-shot Learning from Demonstration Approach Toward a Reciprocal Sign Language-based HRI

Contact Info

Product

Resources

About