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Conference Paper
Adaptive Behavior Generation of Social Robots Based on User Behavior Recognition
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- Authors
- Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, Jaehong Kim
- Issue Date
- 2020-09
- Citation
- International Symposium on Robot and Human Interactive Communication (RO-MAN) 2020, pp.1-4
- Language
- English
- Type
- Conference Paper
- Abstract
- Social robots should understand the user’s behavior and respond appropriately for natural human-robot interaction. Our work is aimed at recognizing the subtle differences in user behavior and generating behavior appropriately. For the user behavior recognition, we use a Kinect v.2 sensor for skeletal tracking and a deep neural network (DNN) for behavior classification. The weights of the DNN are trained using AIRAct2Act, which is a human-human interaction dataset. For the robot behavior generation, we designed several behavior selection rules by referring to the interaction scenarios of the dataset, and then modify the key pose of the selected behavior taking into account the user’s posture, position, and physical characteristics such as height. To demonstrate the effectiveness of the proposed method, we perform experiments using a Pepper robot in the 3D virtual environment. The experimental results show that the proposed method has a 98% accuracy in recognizing the user’s behavior and can naturally change the behavior in a situation where the user’s intention is confused.
- KSP Keywords
- 3d virtual environment, Adaptive behavior, Behavior Generation, Behavior classification, Behavior selection, Deep neural network(DNN), Human-Human Interaction, Human-Robot Interaction(HRI), Natural human-robot interaction, Physical characteristics, User behavior