Emotion-Aware Teaching Robot: Learning to Adjust to User’s Emotional State
Date Issued
2018-09-17
Author(s)
Stojanovska, Frosina
Abstract
Robots today are taking more and more complex roles thus
they are getting smarter and more human-like. One complex function,
specific to social robots, is the role of robots in human-robot interaction. They are helpful in the process of social human-robot interaction
while performing a specific task like teaching, assisting, entertaining, etc.
The ability to recognize emotions has a significant role for social robots.
A robot that can understand emotions could be able to interact according
to that emotion. In this paper, we propose a model for robotic behavior adapting to the user’s emotions. The humanoid robot Nao is used
in the role of emotion-aware teacher for teaching math. Its main purpose is to teach and entertain the user while adapting its behavior to
the user’s emotional state derived from the facial expression. The robot
uses reinforcement learning to learn which action to perform in a specific emotional state. It employs the Q-learning algorithm, maximizing
the next action’s award - a value that depends on the current emotional
state of the user. An experimental study with a selected group of subjects
is conducted to assess the proposed behavior. We evaluated the robot’s
ability to recognize emotions and the subjects’ experience of interacting
with the robot.
they are getting smarter and more human-like. One complex function,
specific to social robots, is the role of robots in human-robot interaction. They are helpful in the process of social human-robot interaction
while performing a specific task like teaching, assisting, entertaining, etc.
The ability to recognize emotions has a significant role for social robots.
A robot that can understand emotions could be able to interact according
to that emotion. In this paper, we propose a model for robotic behavior adapting to the user’s emotions. The humanoid robot Nao is used
in the role of emotion-aware teacher for teaching math. Its main purpose is to teach and entertain the user while adapting its behavior to
the user’s emotional state derived from the facial expression. The robot
uses reinforcement learning to learn which action to perform in a specific emotional state. It employs the Q-learning algorithm, maximizing
the next action’s award - a value that depends on the current emotional
state of the user. An experimental study with a selected group of subjects
is conducted to assess the proposed behavior. We evaluated the robot’s
ability to recognize emotions and the subjects’ experience of interacting
with the robot.
Subjects
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