Robot reacting on basis of user behavior and control method therefor

1. A control method for a robot using an artificial intelligence model, the control method comprising:

acquiring user data related to at least one user;

training the artificial intelligence model to acquire an emotional state for each of the at least one user by inputting the user data related to the at least one user to the artificial intelligence model, the user data comprising at least one image data;

acquiring first data related to a first user, the first data including information regarding a first schedule of the first user;

determining, based on the first data, a first emotional state of the first user by inputting the first data to the trained artificial intelligence model;

controlling the robot to output a first reaction based on the first data and the first emotional state;

determining a character of the robot based on a character area of a character model including a first axis indicating whether an emotion of the robot is positive or negative and a second axis indicating an activity of the robot, wherein the character area of the character model is determined based on a feedback of the first user for the first reaction, a number of interactions with the first user and a service completion rate representing an accuracy of the first reaction of the robot;

acquiring second data related to the first user after determining the character of the robot;

determining, based on acquiring the second data related to the first user, a second emotional state of the first user by inputting the second data to the trained artificial intelligence model; and

controlling the robot to output a second reaction based on the second emotional state and the character of the robot.

2. The control method of claim 1 , further comprising:

detecting, by the robot, a second user;

determining a third emotional state by inputting third data related to the second user to the artificial intelligence model; and

controlling the robot based on a representative reaction corresponding to the determined third emotional state.

3. The control method of claim 1 , wherein training the artificial intelligence model comprises:

clustering a plurality of learning data into a plurality of clusters by inputting the plurality of learning data into the artificial intelligence model; and

labeling emotional states corresponding to respective clusters of the plurality of clusters.

4. The control method of claim 3 , further comprising:

acquiring a variance value of learning data included in a cluster generated by the clustering; and

determining a representative reaction of the cluster by analyzing the acquired variance value.

5. The control method of claim 4 , wherein the representative reaction includes at least one of a facial expression reaction, a motion reaction, or a voice reaction.

6. The control method of claim 4 , wherein controlling the robot to output the first reaction comprises:

determining a robot reaction corresponding to the representative reaction; and

outputting the robot reaction.

7. The control method of claim 6 , wherein the character of the robot is determined according to a number of interactions with the first user, the first emotional state, and a degree of completion of the robot reaction.

8. The control method of claim 1 , wherein the learning data includes at least one of the image data, voice data, data about peripheral users, or data received from peripheral devices.

9. The control method of claim 1 , wherein the artificial intelligence model is learned using unsupervised learning, and

wherein the unsupervised learning uses at least one of a K-mean clustering technique or a gaussian mixture model (GMM) technique.

10. A robot using an artificial intelligence model, the robot comprising:

a processor configured to:

acquire user data related to at least one user;

train the artificial intelligence model to acquire an emotional state for each of the at least one user by inputting the user data related to the at least one user to the artificial intelligence model as learning data, the user data comprising at least one image data;

acquire first data related to a first user, the first data including information regarding a first schedule of the first user;

determine, based on the first data, a first emotional state of the first user by inputting the first data to the trained artificial intelligence model;

output a first reaction based on the first data and the first emotional state;

determine a character of the robot based on a character area of a character model including a first axis indicating whether an emotion of the robot is positive or negative and a second axis indicating an activity of the robot, wherein the character area of the character model is determined based on a feedback of the first user for the first reaction, a number of interactions with the first user and a service completion rate representing an accuracy of the first reaction of the robot;

acquiring second data related to the first user after the character of the robot is determined;

determine, based on acquiring the second data related to the first user, a second emotional state of the first user by inputting the second data to the trained artificial intelligence model; and

output a second reaction based on the second emotional state and the character of the robot.

11. The robot of claim 10 , wherein the processor is further configured to:

detect a second user;

determine a third emotional state by inputting third data related to the second user to the artificial intelligence model; and

control the robot based on a representative reaction corresponding to the determined third emotional state.

12. The robot of claim 10 , wherein the processor is further configured to:

cluster a plurality of learning data into a plurality of clusters by inputting the plurality of learning data into the artificial intelligence model; and

label emotional states corresponding to respective clusters of the plurality of clusters.

13. The robot of claim 12 , wherein the processor is further configured to:

acquire a variance value of learning data included in a cluster generated by the clustering; and

determine a representative reaction of the cluster by analyzing the acquired variance value.

14. The robot of claim 13 , wherein the representative reaction includes at least one of a facial expression reaction, a motion reaction, or a voice reaction.

15. The robot of claim 13 , wherein the processor is further configured to:

determine a robot reaction corresponding to the representative reaction; and

control output of the robot reaction.