International Conference on Social Robotics (ICSR) 2021: Workshop, pp.1-2
Language
English
Type
Conference Paper
Abstract
In order for users to feel familiar with social robots, it is important for social robots to generate non-verbal robot behaviors, such as handshakes. However, the traditional approaches of reproducing pre-coded motions allow users to easily predict the robot’s reaction, giving the impression that the robot is a machine and not a real agent. To enable social robots to learn multiple human-like behaviors from human-human interactions, we proposed an end-to-end learning-based behavior generation method. The Seq2Seq architecture consisting of two long shortterm memory units was adopted. One is for encoding user behavior and the other is for generating the next robot behavior. To demonstrate the effectiveness of our method, two experiments were performed using a humanoid robot, Pepper, in a simulated environment. Experimental results showed that the robot can generate five social behaviors, i.e. bow, stand, handshake, hug, and block face corresponding to user behavior, and adjust its behavior according to the user’s posture.
KSP Keywords
Behavior Generation, End to End(E2E), End-to-end learning, Human interaction, Human-like, Humanoid Robot, Learning-based, Multiple human, Nonverbal behavior, Pre-coded, Robot behavior
Copyright Policy
ETRI KSP Copyright Policy
The materials provided on this website are subject to copyrights owned by ETRI and protected by the Copyright Act. Any reproduction, modification, or distribution, in whole or in part, requires the prior explicit approval of ETRI. However, under Article 24.2 of the Copyright Act, the materials may be freely used provided the user complies with the following terms:
The materials to be used must have attached a Korea Open Government License (KOGL) Type 4 symbol, which is similar to CC-BY-NC-ND (Creative Commons Attribution Non-Commercial No Derivatives License). Users are free to use the materials only for non-commercial purposes, provided that original works are properly cited and that no alterations, modifications, or changes to such works is made. This website may contain materials for which ETRI does not hold full copyright or for which ETRI shares copyright in conjunction with other third parties. Without explicit permission, any use of such materials without KOGL indication is strictly prohibited and will constitute an infringement of the copyright of ETRI or of the relevant copyright holders.
J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
If you have any questions or concerns about these terms of use, or if you would like to request permission to use any material on this website, please feel free to contact us
KOGL Type 4:(Source Indication + Commercial Use Prohibition+Change Prohibition)
Contact ETRI, Research Information Service Section
Privacy Policy
ETRI KSP Privacy Policy
ETRI does not collect personal information from external users who access our Knowledge Sharing Platform (KSP). Unathorized automated collection of researcher information from our platform without ETRI's consent is strictly prohibited.
[Researcher Information Disclosure] ETRI publicly shares specific researcher information related to research outcomes, including the researcher's name, department, work email, and work phone number.
※ ETRI does not share employee photographs with external users without the explicit consent of the researcher. If a researcher provides consent, their photograph may be displayed on the KSP.