ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Conference Paper End-to-End Learning of Social Behaviors for Humanoid Robots
Cited 7 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Woo-Ri Ko, Jaeyeon Lee, Minsu Jang, Jaehong Kim
Issue Date
2020-10
Citation
International Conference on Systems, Man, and Cybernetics (SMC) 2020, pp.1-6
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/SMC42975.2020.9283177
Abstract
Social robots should understand the user's nonverbal behavior and respond appropriately. Machine learning is one way of implementing the social intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. This paper proposes an end-to-end machine learning method to learn social behaviors for humanoid robots. We adapt sequence-to-sequence architecture consisting of two long short-term memory (LSTM) units. One is an LSTM encoder for encoding the previous sequence of human poses, and the other is an LSTM decoder for generating the next sequence of robot poses. The weights of the LSTMs are trained using human-human interaction data such as greeting and handshaking. The trained model is implemented in a humanoid robot, Pepper, to show its feasibility. Experimental results show that the robot can generate gestures appropriate to the situation and recognize subtle differences in user behavior. In addition, when a user's behavior changes, the transition to another behavior occurs naturally.
KSP Keywords
End to End(E2E), End-to-end learning, Humanoid Robot, Interaction data, Machine Learning Methods, Nonverbal behavior, Sequence architecture, Social Intelligence, Social behavior, User Behavior, User's behavior