JP2024159569A - Behavior Control System - Google Patents

Abstract

To cause a robot to perform an appropriate action in response to a user's situation.
[Solution] A robot behavior control system applied to support child care, comprising a user emotion determination unit which determines an emotion value indicating the emotion of the user who is the subject of child care, a robot emotion determination unit which determines an emotion value indicating the emotion of the robot, and a behavior determination unit which determines the behavior of the robot based on at least one of the user's emotion value and the robot's emotion value and information obtained by a sentence generation model having an interaction function that allows the user to interact with the robot, and the behavior determination unit determines the content of play that the robot will suggest to the user who is the subject of child care.
[Selection diagram] Figure 9

Description

The present invention relates to a behavior control system.

Patent Document 1 discloses a technology for determining an appropriate robot behavior in response to a user's state. The conventional technology in Patent Document 1 recognizes the user's reaction when the robot performs a specific action, and if the robot is unable to determine an action to be taken in response to the recognized user's reaction, it updates the robot's behavior by receiving information about an action appropriate to the recognized user's state from a server.

Patent No. 6053847

However, conventional technology leaves room for improvement in enabling robots to perform appropriate actions in response to user actions.

The behavior control system according to the present invention is a behavior control system for a robot applied to support childcare, and includes a user emotion determination unit that determines an emotion value indicating the emotion of a user who is the subject of childcare, a robot emotion determination unit that determines an emotion value indicating the emotion of a robot, and a behavior determination unit that determines the behavior of the robot based on at least one of the emotion value of the user and the emotion value of the robot and information acquired by a sentence generation model having a dialogue function that allows the user and the robot to converse with each other, and the behavior determination unit determines the content of play that the robot proposes to the user who is the subject of childcare.

The present invention makes it possible to have a robot perform appropriate actions depending on the user's situation.

FIG. 1 is a diagram illustrating an example of a system 5 according to an embodiment of the present invention. FIG. 2 is a diagram illustrating a functional configuration of the robot 100. FIG. 2 is a diagram illustrating an example of an operation flow of the robot 100. FIG. 12 is a diagram illustrating an example of the hardware configuration of a computer 1200. FIG. 3 shows an emotion map 300 onto which multiple emotions are mapped. FIG. 9 illustrates an emotion map 900 onto which multiple emotions are mapped. FIG. 4 is a diagram illustrating an emotion table of the robot 100. FIG. 4 is a diagram illustrating an emotion table of a user 10. FIG. 13 is a flowchart illustrating an example of the operation of the robot 100 when applied to a childcare robot.

The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

A. This embodiment FIG. 1 shows an example of a system 5 according to this embodiment. The system 5 includes a robot 100, a robot 101, a robot 102, and a server 300. The user 10, the user 11, and the user 12 are users of the robot 100, the robot 101, and the robot 102, respectively. The user 10, the user 11, and the user 12 are, for example, family members of the house in which the robot 100 is placed, or other people who visit the house. The robots 100, the robot 101, and the robot 102 can be placed at the reception desk of a store or an office, and can be used to respond to visiting customers. In the description of this embodiment, the robots 101 and 102 have substantially the same functions as the robot 100. Therefore, the system 5 will be described by mainly focusing on the functions of the robot 100.

The robot 100 converses with the user 10 and provides images to the user 10. At this time, the robot 100 cooperates with a server 300 and the like with which it can communicate via the communication network 20 to converse with the user 10 and provide images and the like to the user 10. For example, the robot 100 not only learns appropriate conversation by itself, but also cooperates with the server 300 to learn so as to be able to have a more appropriate conversation with the user 10. The robot 100 also records the captured image data of the user 10 in the server 300, and requests the image data and the like from the server 300 as necessary to provide it to the user 10. In FIG. 1, the robot 100 is assumed to be a running robot capable of moving independently, but it can be applied to any terminal (for example, an in-vehicle terminal or a mobile terminal) equipped with an AI emotion engine or a sentence generation model as described below.

The robot 100 is equipped with an AI emotion engine (described below) that has the function of generating artificial human emotions, and determines and stores emotion values that represent the type of emotion it feels. For example, the robot 100 has emotion values that represent the strength of each of the emotions: "happiness," "anger," "sorrow," "pleasure," "discomfort," "relief," "anxiety," "sadness," "excitement," "worry," "relief," "fulfillment," "emptiness," and "neutral." When the robot 100 converses with the user 10 when its excitement emotion value is high, for example, it speaks at a fast speed. In this way, the robot 100 can express its own emotions through its actions.

The robot 100 may be configured to determine the behavior of the robot 100 corresponding to the emotion of the user 10 by matching a sentence generation model (chat engine) with an AI emotion engine (emotion engine). Specifically, the robot 100 may be configured to recognize the behavior of the user 10, determine the emotion of the user 10 regarding the user's behavior, and determine the behavior of the robot 100 corresponding to the determined emotion.

More specifically, when the robot 100 recognizes the behavior of the user 10, the robot 100 automatically generates the behavioral content that the robot 100 should take in response to the behavior of the user 10 using a preset chat engine. The chat engine may be interpreted as an algorithm and calculation for automatic dialogue processing by text. The chat engine is publicly known as disclosed in, for example, JP 2018-081444 A and chatGPT (Internet search <URL: https://openai.com/blog/chatgpt>), so a detailed description thereof will be omitted. Such a chat engine is configured by a large-scale language model (LLM: Large Language Model). As described above, in this embodiment, by combining a large-scale language model and an emotion engine, it is possible to reflect the emotions of the user 10 and the robot 100 and various linguistic information in the behavior of the robot 100. In other words, according to this embodiment, a synergistic effect can be obtained by combining a chat engine and an emotion engine.

The robot 100 also has a function of recognizing the behavior of the user 10. The robot 100 recognizes the behavior of the user 10 by analyzing the facial image of the user 10 acquired by the camera function and the voice of the user 10 acquired by the microphone function. The robot 100 determines the behavior to be performed by the robot 100 based on the recognized behavior of the user 10, etc.

The robot 100 stores rules that define the actions that the robot 100 will take based on the emotions of the user 10, the emotions of the robot 100, and the actions of the user 10, and performs various actions according to the rules.

Specifically, the robot 100 has reaction rules for determining the behavior of the robot 100 based on the emotions of the user 10, the emotions of the robot 100, and the behavior of the user 10. For example, the reaction rules define the behavior of the robot 100 as "laughing" when the behavior of the user 10 is "laughing". The reaction rules also define the behavior of the robot 100 as "apologizing" when the behavior of the user 10 is "angry". The reaction rules also define the behavior of the robot 100 as "answering" when the behavior of the user 10 is "asking a question". The reaction rules also define the behavior of the robot 100 as "calling out" when the behavior of the user 10 is "sad".

When the robot 100 recognizes that the behavior of the user 10 is "angry" based on the reaction rules, the robot 100 selects the behavior of "apologizing" defined in the reaction rules as the behavior to be executed by the robot 100. For example, when the robot 100 selects the behavior of "apologizing", the robot 100 performs the motion of "apologizing" and outputs a voice expressing the words "apologize".

In addition, when the emotion of the robot 100 is "normal" (i.e. "Happy" = 0, "Anger" = 0, "Sad" = 0, "Happy" = 0) and the condition that the user 10 is in is "alone and looks lonely", it is defined that the emotion of the robot 100 will change to "worried" and that the robot 100 will be able to execute the action of "calling out".

When the robot 100 recognizes based on the reaction rules that the current emotion of the robot 100 is "normal" and that the user 10 is alone and seems lonely, the robot 100 increases the emotion value of "sadness" of the robot 100. In addition, the robot 100 selects the action of "calling out" defined in the reaction rules as the action to be performed toward the user 10. For example, when the robot 100 selects the action of "calling out", it converts the words "What's wrong?", which express concern, into a worried voice and outputs it.

The robot 100 also transmits to the server 300 user reaction information indicating that this action has elicited a positive reaction from the user 10. The user reaction information includes, for example, the user action of "getting angry," the robot 100 action of "apologizing," the fact that the user 10's reaction was positive, and the attributes of the user 10.

The server 300 stores the user reaction information received from the robot 100. The server 300 receives and stores user reaction information not only from the robot 100, but also from each of the robots 101 and 102. The server 300 then analyzes the user reaction information from the robots 100, 101, and 102, and updates the reaction rules.

The robot 100 receives the updated reaction rules from the server 300 by inquiring about the updated reaction rules from the server 300. The robot 100 incorporates the updated reaction rules into the reaction rules stored in the robot 100. This allows the robot 100 to incorporate the reaction rules acquired by the robot 101, the robot 102, etc. into its own reaction rules.

Figure 2 shows a schematic functional configuration of the robot 100. The robot 100 has a sensor unit 200, a sensor module unit 210, a storage unit 220, a user state recognition unit 230, a user emotion determination unit 231, a robot emotion determination unit 232, a behavior recognition unit 234, a behavior determination unit 236, a memory control unit 238, a behavior control unit 250, a control target 252, and a communication processing unit 280.

The control target 252 includes a display device, a speaker, and LEDs in the eyes mounted on the robot 100, as well as motors for driving the arms, hands, legs, etc. The posture and gestures of the robot 100 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 100 can be expressed by controlling these motors. In addition, the facial expressions of the robot 100 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 100. The posture, gestures, and facial expressions of the robot 100 are examples of the attitude of the robot 100.

The sensor unit 200 includes a microphone 201, a 3D depth sensor 202, a 2D camera 203, and a distance sensor 204. The microphone 201 continuously detects sound and outputs sound data. The microphone 201 may be provided on the head of the robot 100 and may have a function of performing binaural recording. The 3D depth sensor 202 detects the contour of an object by continuously irradiating an infrared pattern and analyzing the infrared pattern from infrared images continuously captured by the infrared camera. The 2D camera 203 is an example of an image sensor. The 2D camera 203 captures images using visible light and generates visible light video information. The distance sensor 204 detects the distance to an object by irradiating, for example, a laser or ultrasonic waves. The sensor unit 200 may also include a clock, a gyro sensor, a touch sensor, a sensor for motor feedback, and the like.

Note that, among the components of the robot 100 shown in FIG. 2, the components other than the control target 252 and the sensor unit 200 are examples of components that the behavior control system of the robot 100 has. The behavior control system of the robot 100 controls the control target 252.

The storage unit 220 includes reaction rules 221 and history data 222. The history data 222 includes the user 10's past emotional values and behavioral history. The emotional values and behavioral history are recorded for each user 10, for example, by being associated with the user 10's identification information. At least a part of the storage unit 220 is implemented by a storage medium such as a memory. It may include a person DB that stores the face image of the user 10, the attribute information of the user 10, and the like. Note that the functions of the components of the robot 100 shown in FIG. 2, excluding the control target 252, the sensor unit 200, and the storage unit 220, can be realized by the CPU operating based on a program. For example, the functions of these components can be implemented as the operation of the CPU by the operating system (OS) and a program that operates on the OS.

The sensor module unit 210 includes a voice emotion recognition unit 211, a speech understanding unit 212, a facial expression recognition unit 213, and a face recognition unit 214. Information detected by the sensor unit 200 is input to the sensor module unit 210. The sensor module unit 210 analyzes the information detected by the sensor unit 200 and outputs the analysis result to the user state recognition unit 230.

The voice emotion recognition unit 211 of the sensor module unit 210 analyzes the voice of the user 10 detected by the microphone 201 and recognizes the emotions of the user 10. For example, the voice emotion recognition unit 211 extracts features such as frequency components of the voice, and recognizes the voice and emotions of the user 10 based on the extracted features. The speech understanding unit 212 analyzes the voice of the user 10 detected by the microphone 201 and outputs text information representing the content of the user 10's utterance.

The facial expression recognition unit 213 recognizes the facial expression and emotions of the user 10 from the image of the user 10 captured by the 2D camera 203. For example, the facial expression recognition unit 213 recognizes the facial expression and emotions of the user 10 based on the shape, positional relationship, etc. of the eyes and mouth.

The face recognition unit 214 recognizes the face of the user 10. The face recognition unit 214 recognizes the user 10 by matching a face image stored in a person DB (not shown) with a face image of the user 10 captured by the 2D camera 203.

The user state recognition unit 230 recognizes the state of the user 10 based on the information analyzed by the sensor module unit 210. For example, it mainly performs processing related to perception using the analysis results of the sensor module unit 210. For example, it generates perceptual information such as "Daddy is alone" or "There is a 90% chance that Daddy is not smiling." It then performs processing to understand the meaning of the generated perceptual information. For example, it generates semantic information such as "Daddy is alone and looks lonely."

The user emotion determination unit 231 includes an emotion recognition engine 231a, and determines an emotion value indicating the emotion of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230. For example, the information analyzed by the sensor module unit 210 and the recognized state of the user 10 are input to a pre-trained neural network to obtain an emotion value indicating the emotion of the user 10.

Here, the emotion value indicating the emotion of user 10 is a value indicating the positive or negative emotion of the user. For example, if the user's emotion is a cheerful emotion accompanied by a sense of pleasure or comfort, such as "joy," "pleasure," "comfort," "relief," "excitement," "relief," and "fulfillment," the value is positive, and the more cheerful the emotion, the larger the value. If the user's emotion is an unpleasant emotion, such as "anger," "sorrow," "discomfort," "anxiety," "sorrow," "worry," and "emptiness," the value is negative, and the more unpleasant the emotion, the larger the absolute value of the negative value. If the user's emotion is none of the above ("normal"), the value is 0.

The robot emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230. The robot emotion determination unit 232 includes an endocrine control unit 232a and an emotion generation engine 232b. The endocrine control unit 232a adjusts the parameters of the neural network used in the emotion generation engine 232b using the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230. For example, the endocrine control unit 232a adjusts a parameter corresponding to the amount of dopamine released. Dopamine is an example of an endocrine substance. An endocrine substance means a substance that is secreted in the body and transmits a signal, such as a neurotransmitter or hormone. However, the endocrine substance of the robot 100 itself is one of the pieces of information that affect the operation of the robot 100, and does not mean that the robot 100 actually generates an endocrine substance. In addition, the use of endocrine secretions to determine a robot's own emotions is publicly known, as disclosed in, for example, JP 2018-81583 A, and therefore a detailed explanation will be omitted.

The emotion generation engine 232b uses a neural network to determine an emotion value that indicates the emotion of the robot 100, based on the information analyzed by the sensor module section 210, the state of the user 10 recognized by the user state recognition section 230, and parameters adjusted by the endocrine control section 232a. The emotion value of the robot 100 includes emotion values for each of a number of emotion classifications, and in this embodiment, values (0 to 5) that indicate the strength of each of "happiness," "anger," "sorrow," and "pleasure" are assumed.

To give a specific example, if the user state recognition unit 230 recognizes that the user 10 looks lonely, the endocrine control unit 232a controls to increase the parameter corresponding to the emotion value of "sadness" of the robot 100, and as a result, the robot emotion determination unit 232 increases the emotion value of "sadness" of the robot 100. Also, if the user state recognition unit 230 recognizes that the user 10 is smiling, the endocrine control unit 232a controls to increase the parameter corresponding to the emotion value of "happy" of the robot 100, and as a result, the robot emotion determination unit 232 increases the emotion value of "happy" of the robot 100.

The robot emotion determination unit 232 may determine the emotion value indicating the emotion of the robot 100 by further considering the state of the robot 100. For example, when the battery level of the robot 100 is low or when the surrounding environment of the robot 100 is pitch black, the endocrine control unit 232a controls to increase the parameter corresponding to the emotion value of "sadness" of the robot 100, and the robot emotion determination unit 232 may increase the emotion value of "sadness" of the robot 100. Furthermore, when the user 10 continues to talk to the robot 100 despite the battery level being low, the endocrine control unit 232a controls to increase the parameter corresponding to the emotion value of "anger" of the robot 100, and the robot emotion determination unit 232 may increase the emotion value of "anger" of the robot 100.

The behavior recognition unit 234 recognizes the behavior of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230. For example, the information analyzed by the sensor module unit 210 and the recognized state of the user 10 are input to a pre-trained neural network, the probability of each of a number of predetermined behavioral categories (e.g., "laughing," "anger," "asking a question," "sad") is obtained, and the behavioral category with the highest probability is recognized as the behavior of the user 10.

As described above, in this embodiment, the robot 100 identifies the user 10 and acquires the contents of the user's utterance. When acquiring and using the contents of the utterance, the robot 100 obtains the necessary consent in accordance with laws and regulations from the user 10, and the behavior control system of the robot 100 according to this embodiment takes into consideration the protection of the personal information and privacy of the user 10.

The behavior determination unit 236 determines an action corresponding to the behavior of the user 10 recognized by the behavior recognition unit 234 based on the current emotion value of the user 10 determined by the user emotion determination unit 231, the history data 222 of past emotion values determined by the user emotion determination unit 231 before the current emotion value of the user 10 was determined, and the emotion value of the robot 100 determined by the robot emotion determination unit 232. In this embodiment, the behavior determination unit 236 uses one most recent emotion value included in the history data 222 as the past emotion value of the user 10, but the disclosed technology is not limited to this aspect. For example, the behavior determination unit 236 may use the most recent multiple emotion values as the past emotion value of the user 10, or may use an emotion value from a unit period ago, such as one day ago. In addition, the behavior determination unit 236 may determine an action corresponding to the behavior of the user 10 by further considering not only the current emotion value of the robot 100 but also the history of the past emotion values of the robot 100. The behavior determined by the behavior determination unit 236 includes gestures performed by the robot 100 or the contents of speech uttered by the robot 100.

The behavior decision unit 236 according to this embodiment decides the behavior of the robot 100 as the behavior corresponding to the behavior of the user 10, based on a combination of the past and current emotion values of the user 10, the emotion value of the robot 100, the behavior of the user 10, and the reaction rules 221. For example, when the past emotion value of the user 10 is a positive value and the current emotion value is a negative value, the behavior decision unit 236 decides the behavior for changing the emotion value of the user 10 to a positive value as the behavior corresponding to the behavior of the user 10.

The reaction rules 221 define the behavior of the robot 100 according to a combination of the past and current emotion values of the user 10, the emotion value of the robot 100, and the behavior of the user 10. For example, when the past emotion value of the user 10 is a positive value and the current emotion value is a negative value, and the behavior of the user 10 is sad, a combination of gestures and speech content when asking a question to encourage the user 10 with gestures is defined as the behavior of the robot 100.

For example, the reaction rule 221 defines behaviors of the robot 100 for all combinations of patterns of the emotion values of the robot 100 (1296 patterns, which are the fourth power of six values of "joy", "anger", "sorrow", and "pleasure", from "0" to "5"); combination patterns of the past emotion values and the current emotion values of the user 10; and behavior patterns of the user 10. That is, for each pattern of the emotion values of the robot 100, behaviors of the robot 100 are defined according to the behavior patterns of the user 10 for each of a plurality of combinations of the past emotion values and the current emotion values of the user 10, such as negative values and negative values, negative values and positive values, positive values and negative values, positive values and positive values, negative values and normal values, and normal values and normal values. Note that the behavior determining unit 236 may transition to an operation mode in which the behavior of the robot 100 is determined using the history data 222, when the user 10 makes an utterance intending to continue a conversation from a past topic, such as "I want to talk about that topic we talked about last time."
The reaction rules 221 may prescribe at least one of a gesture and a statement as the behavior of the robot 100 for each of the patterns (1296 patterns) of the emotion value of the robot 100. Alternatively, the reaction rules 221 may prescribe at least one of a gesture and a statement as the behavior of the robot 100 for each group of patterns of the emotion value of the robot 100.

For each gesture included in the behavior of the robot 100 defined in the reaction rules 221, the strength of the gesture is predefined. For each utterance content included in the behavior of the robot 100 defined in the reaction rules 221, the strength of the utterance content is predefined.

The memory control unit 238 determines whether or not to store data including the behavior of the user 10 in the history data 222 based on the predetermined behavior strength for the behavior determined by the behavior determination unit 236 and the emotion value of the robot 100 determined by the robot emotion determination unit 232.
Specifically, when a total intensity value, which is the sum of the sum of the emotion values for each of the multiple emotion classifications of the robot 100, the predetermined intensity for the gestures included in the behavior determined by the behavior determination unit 236, and the predetermined intensity for the speech content included in the behavior determined by the behavior determination unit 236, is equal to or greater than a threshold value, it is decided to store data including the behavior of the user 10 in the history data 222.

When the memory control unit 238 decides to store data including the behavior of the user 10 in the history data 222, it stores in the history data 222 the behavior determined by the behavior determination unit 236, information analyzed by the sensor module unit 210 from the present time up to a certain period of time ago (e.g., all surrounding information such as data on the sound, images, smells, etc. of the scene), and the state of the user 10 recognized by the user state recognition unit 230 (e.g., the facial expression, emotions, etc. of the user 10).

The behavior control unit 250 controls the control target 252 based on the behavior determined by the behavior determination unit 236. For example, when the behavior determination unit 236 determines an behavior that includes speaking, the behavior control unit 250 outputs a sound from a speaker included in the control target 252. At this time, the behavior control unit 250 may determine the speaking speed of the sound based on the emotion value of the robot 100. For example, the behavior control unit 250 determines a faster speaking speed as the emotion value of the robot 100 increases. In this way, the behavior control unit 250 determines the execution form of the behavior determined by the behavior determination unit 236 based on the emotion value determined by the robot emotion determination unit 232.

The behavior control unit 250 may recognize a change in the user 10's emotions in response to the execution of the behavior determined by the behavior determination unit 236. For example, the change in emotions may be recognized based on the voice or facial expression of the user 10. Alternatively, the change in emotions may be recognized based on the detection of an impact by a touch sensor included in the sensor unit 200. If an impact is detected by the touch sensor included in the sensor unit 200, the user 10's emotions may be recognized as having worsened, and if the detection result of the touch sensor included in the sensor unit 200 indicates that the user 10 is smiling or happy, the user 10's emotions may be recognized as having improved. Information indicating the user 10's reaction is output to the communication processing unit 280.

In addition, after the behavior control unit 250 executes the behavior determined by the behavior determination unit 236 in the execution form determined according to the emotion of the robot 100, it controls the robot emotion determination unit 232 to further change the emotion value of the robot 100 based on the user's reaction to the execution of the behavior. Specifically, the robot emotion determination unit 232 increases the emotion value of "happiness" of the robot 100 when the user's reaction to the behavior determined by the behavior determination unit 236 being performed on the user in the execution form determined by the behavior control unit 250 is not bad. In addition, the robot emotion determination unit 232 increases the emotion value of "sadness" of the robot 100 when the user's reaction to the behavior determined by the behavior determination unit 236 being performed on the user in the execution form determined by the behavior control unit 250 is bad.

Furthermore, the behavior control unit 250 expresses the emotion of the robot 100 based on the determined emotion value of the robot 100. For example, when the behavior control unit 250 increases the emotion value of "happiness" of the robot 100, it controls the control object 252 to make the robot 100 perform a happy gesture. When the behavior control unit 250 increases the emotion value of "sadness" of the robot 100, it controls the control object 252 to make the robot 100 assume a droopy posture.

The communication processing unit 280 is responsible for communication with the server 300. As described above, the communication processing unit 280 transmits user reaction information to the server 300. In addition, the communication processing unit 280 receives updated reaction rules from the server 300. When the communication processing unit 280 receives updated reaction rules from the server 300, it updates the reaction rules 221.

The server 300 communicates between the robot 100, the robot 101, and the robot 102 and the server 300, receives user reaction information sent from the robot 100, and updates the reaction rules based on the reaction rules including actions that have generated positive reactions. The functions of the server 300 may be implemented by one or more computers. At least a portion of the functions of the server 300 may be implemented by a virtual machine. At least a portion of the functions of the server 300 may be implemented in the cloud.

Figure 3 shows an example of an outline of an operation flow relating to the operation of determining an action in the robot 100. The operation flow shown in Figure 3 is executed repeatedly. At this time, it is assumed that information analyzed by the sensor module unit 210 is input. Note that "S" in the operation flow indicates the step that is executed.

First, in step S100, the user state recognition unit 230 recognizes the state of the user 10 based on the information analyzed by the sensor module unit 210.

In step S102, the user emotion determination unit 231 determines an emotion value indicating the emotion of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230.

In step S103, the robot emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230. The robot emotion determination unit 232 adds the determined emotion value of the user 10 to the history data 222.

In step S104, the behavior recognition unit 234 recognizes the behavior classification of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the user state recognition unit 230.

In step S106, the behavior decision unit 236 decides the behavior of the robot 100 based on a combination of the current emotion value of the user 10 determined in step S102 and the past emotion values included in the history data 222, the emotion value of the robot 100, the behavior of the user 10 recognized by the behavior recognition unit 234, and the reaction rules 221.

In step S108, the behavior control unit 250 controls the control object 252 based on the behavior determined by the behavior determination unit 236.

In step S110, the memory control unit 238 calculates a total intensity value based on the predetermined action intensity for the action determined by the action determination unit 236 and the emotion value of the robot 100 determined by the robot emotion determination unit 232.

In step S112, the storage control unit 238 determines whether the total intensity value is equal to or greater than the threshold value. If the total intensity value is less than the threshold value, the process ends without storing data including the behavior of the user 10 in the history data 222. On the other hand, if the total intensity value is equal to or greater than the threshold value, the process proceeds to step S114.

In step S114, the behavior determined by the behavior determination unit 236, the information analyzed by the sensor module unit 210 from the present time up to a certain period of time ago, and the state of the user 10 recognized by the user state recognition unit 230 are stored in the history data 222.

As described above, according to the robot 100, an emotion value indicating the emotion of the robot 100 is determined based on the user state, and whether or not to store data including the behavior of the user 10 in the history data 222 is determined based on the emotion value of the robot 100. This makes it possible to reduce the capacity of the history data 222 that stores data including the behavior of the user 10. For example, when the robot 100 determines that the user state 10 years from now is the same as that 10 years ago, the robot 100 can present to the user 10 all kinds of peripheral information, such as the state of the user 10 10 years ago (e.g., the facial expression, emotions, etc. of the user 10), and data on the sound, image, smell, etc. of the location.

According to the robot 100, the robot 100 can be made to perform an appropriate action in response to the action of the user 10. Conventionally, the user's actions were classified and the action including the robot's facial expression and appearance was determined. In contrast, the robot 100 determines the current emotional value of the user 10 and performs an action on the user 10 based on the past emotional value and the current emotional value. Therefore, for example, if the user 10 who was cheerful yesterday is depressed today, the robot 100 can make an utterance such as "You were cheerful yesterday, but what's wrong with you today?" The robot 100 can also make an utterance with gestures. For example, if the user 10 who was depressed yesterday is cheerful today, the robot 100 can make an utterance such as "You were depressed yesterday, but you look cheerful today, don't you?" For example, if the user 10 who was cheerful yesterday is more cheerful today than yesterday, the robot 100 can make an utterance such as "You're more cheerful today than yesterday. Has something better happened than yesterday?" Furthermore, for example, the robot 100 can say to a user 10 whose emotion value is equal to or greater than 0 and whose emotion value fluctuation range continues to be within a certain range, "You've been feeling stable lately, which is good."

For example, the robot 100 can ask the user 10, "Did you finish the homework I told you about yesterday?", and if the user 10 responds, "I did it," it can utter a positive utterance such as "Great!" and make a positive gesture such as clapping or a thumbs up. For example, when the user 10 utters, "The presentation you gave the day before yesterday went well," the robot 100 can utter a positive utterance such as "You did a great job!" and make the above-mentioned positive gesture. In this way, the robot 100 can be expected to make the user 10 feel a sense of closeness to the robot 100 by performing actions based on the state history of the user 10.

In the above embodiment, the robot 100 recognizes the user 10 using a facial image of the user 10, but the disclosed technology is not limited to this aspect. For example, the robot 100 may recognize the user 10 using a voice emitted by the user 10, an email address of the user 10, an SNS ID of the user 10, or an ID card with a built-in wireless IC tag that the user 10 possesses.

The robot 100 is an example of an electronic device equipped with a behavior control system. The application of the behavior control system is not limited to the robot 100, and the behavior control system can be applied to various electronic devices. Furthermore, the functions of the server 300 may be implemented by one or more computers. At least some of the functions of the server 300 may be implemented by a virtual machine. Furthermore, at least some of the functions of the server 300 may be implemented in the cloud.

Figure 4 shows an example of a hardware configuration of a computer 1200 functioning as the robot 100 and the server 300. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of an apparatus according to the present embodiment, or to execute operations or one or more "parts" associated with an apparatus according to the present embodiment, and/or to execute a process or steps of the process according to the present embodiment. Such a program can be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphics controller 1216, which are connected to each other by a host controller 1210. The computer 1200 also includes input/output units such as a communication interface 1222, a storage device 1224, a DVD drive 1226, and an IC card drive, which are connected to the host controller 1210 via an input/output controller 1220. The DVD drive 1226 may be a DVD-ROM drive, a DVD-RAM drive, or the like. The storage device 1224 may be a hard disk drive, a solid state drive, or the like. The computer 1200 also includes a ROM 1230 and a legacy input/output unit such as a keyboard, which are connected to the input/output controller 1220 via an input/output chip 1240.

The CPU 1212 operates according to the programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into itself, and causes the image data to be displayed on the display device 1218.

The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive 1226 reads programs or data from a DVD-ROM 1227 or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and/or writes programs and data to an IC card.

ROM 1230 stores therein a boot program or the like executed by computer 1200 upon activation, and/or a program that depends on the hardware of computer 1200. I/O chip 1240 may also connect various I/O units to I/O controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

The programs are provided by a computer-readable storage medium such as a DVD-ROM 1227 or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. The information processing described in these programs is read by the computer 1200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by realizing the operation or processing of information according to the use of the computer 1200.

For example, when communication is performed between computer 1200 and an external device, CPU 1212 may execute a communication program loaded into RAM 1214 and instruct communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of CPU 1212, communication interface 1222 reads transmission data stored in a transmission buffer area provided in RAM 1214, storage device 1224, DVD-ROM 1227, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area or the like provided on the recording medium.

The CPU 1212 may also cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, DVD drive 1226 (DVD-ROM 1227), IC card, etc. to be read into the RAM 1214, and perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and may undergo information processing. The CPU 1212 may perform various types of processing on the data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search/replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and writes back the results to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. in the recording medium. For example, when multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

The above-described programs or software modules may be stored in a computer-readable storage medium on the computer 1200 or in the vicinity of the computer 1200. In addition, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

The blocks in the flowcharts and block diagrams in this embodiment may represent stages of a process in which an operation is performed or "parts" of a device responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable storage medium, and/or a processor provided with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuitry may include digital and/or analog hardware circuits and may include integrated circuits (ICs) and/or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements, such as, for example, field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs).

A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture that includes instructions that can be executed to create means for performing the operations specified in the flowchart or block diagram. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray disks, memory sticks, integrated circuit cards, and the like.

The computer readable instructions may include either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, JAVA, C++, etc., and conventional procedural programming languages such as the "C" programming language or similar programming languages.

The computer-readable instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, or a programmable circuit, either locally or over a local area network (LAN), a wide area network (WAN), such as the Internet, so that the processor of the general-purpose computer, special-purpose computer, or other programmable data processing apparatus, or the programmable circuit, executes the computer-readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

(Determining emotions using emotion maps)
The user emotion determination unit 231 may determine the user's emotion according to a specific mapping. Specifically, the user emotion determination unit 231 may use the emotion recognition engine 231a to determine the user's emotion according to an emotion map (see FIG. 5), which is a specific mapping. Similarly, the robot emotion determination unit 232 may use the emotion generation engine 232b to determine the emotion of the robot 100 according to an emotion map (see FIG. 5), which is a specific mapping. Note that when there is no need to particularly distinguish between the emotion recognition engine 231a and the emotion generation engine 232b, they will be collectively referred to as emotion engines.

5 is a diagram showing an emotion map 300 on which multiple emotions are mapped. In the emotion map 300, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from mental states are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 300, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

(1) For example, if the emotion engine detects emotions at about 100 msec, the frequency of the reaction action (e.g., a backchannel) of the robot 100 may be set to at least the same timing as the emotion engine's detection frequency (100 msec), or may be set to an earlier timing. The emotion engine's detection frequency may be interpreted as the sampling rate.

By detecting emotions in about 100 msec and immediately performing a corresponding reaction (e.g., a backchannel), unnatural backchannels can be avoided, and a natural dialogue that reads the atmosphere can be realized. The reaction (backchannel, etc.) is performed according to the directionality and the degree (strength) of the mandala in the robot's 100 emotion map 300. Note that the detection frequency (sampling rate) of the emotion recognition engine 231a is not limited to 100 ms, and may be changed according to the situation (e.g., when playing sports), the age of the user, etc.

(2) The directionality of emotions and the strength of their intensity may be preset in reference to the emotion map 300, and the movement of the interjections and the strength of the interjections may be set. For example, if the robot 100 feels a sense of stability or security, the robot 100 may nod and continue listening. If the robot 100 feels anxious, confused, or suspicious, the robot 100 may tilt its head or stop shaking its head.

These emotions are distributed in the three o'clock direction on emotion map 300, and usually fluctuate between relief and anxiety. In the right half of emotion map 300, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

(3) If the robot 100 feels good after being praised, the filler "ah" may be inserted before the line, and if the robot 100 feels hurt after receiving harsh words, the filler "ugh!" may be inserted before the line. Also, a physical reaction such as the robot 100 crouching down while saying "ugh!" may be included. These emotions are distributed around 9 o'clock on the emotion map 300.

(4) In the left half of the emotion map 300, internal sensations (reactions) are more important than situational awareness. This can give the impression that the person is reacting unconsciously.

When the robot 100 feels a favorable feeling in its situational awareness while also feeling an internal sensation (reaction) of a sense of satisfaction, the robot 100 may nod deeply while looking at the other person, or may say "uh-huh." In this way, the robot 100 may generate a behavior that shows a balanced favorable feeling toward the other person, that is, acceptance and tolerance toward the other person. Such emotions are distributed around 12 o'clock on the emotion map 300.

Conversely, even when the robot 100 is aware of a situation while experiencing an internal sensation (reaction) of discomfort, the robot 100 may shake its head when it feels disgust, or turn the eye LEDs red and glare at the other person when it feels hatred. These types of emotions are distributed around the 6 o'clock position on the emotion map 300.

(5) The inside of emotion map 300 represents what is going on inside one's mind, while the outside of emotion map 300 represents behavior, so the further out on emotion map 300 you go, the more visible the emotions become (the more they are expressed in behavior).

(6) When listening to someone with a sense of relief, which is distributed around the 3 o'clock area of the emotion map 300, the robot 100 may lightly nod its head and say "hmm," but when it comes to love, which is distributed around 12 o'clock, it may nod vigorously, nodding its head deeply.

The user emotion determination unit 231 inputs the information analyzed by the sensor module unit 210 and the recognized state of the user 10 into a pre-trained neural network, and uses the emotion recognition engine 231a to obtain emotion values indicating each emotion shown in the emotion map 300 to determine the emotion of the user 10. This neural network is pre-trained based on multiple learning data that are combinations of the information analyzed by the sensor module unit 210 and the recognized state of the user 10, and emotion values indicating each emotion shown in the emotion map 300. In addition, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Figure 6. Figure 6 shows an example in which multiple emotions such as "peace of mind," "calm," and "reassuring" have similar emotion values.

On the other hand, the robot emotion determination unit 232 inputs the information analyzed by the sensor module unit 210, the state of the user 10 recognized by the user state recognition unit 230, and the state of the robot 100 into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 300 using the emotion generation engine 232b, and determines the emotion of the robot 100. This neural network is pre-trained based on a plurality of learning data that are combinations of the information analyzed by the sensor module unit 210, the recognized state of the user 10, and the state of the robot 100, and the emotion values indicating each emotion shown in the emotion map 300. For example, the neural network is trained based on learning data that indicates that when it is recognized from the output of a touch sensor (not shown) that the robot 100 is being stroked by the user 10, the emotion value becomes "3" for "happiness," and learning data that indicates that when it is recognized from the output of an acceleration sensor (not shown) that the robot 100 is being hit by the user 10, the emotion value becomes "3" for "anger." Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as shown in the emotion map 900 in Figure 6.

The behavior decision unit 236 generates the robot's behavior by adding fixed sentences to ask about the robot's behavior corresponding to the user's behavior to the text representing the user's behavior, the user's emotions, and the robot's emotions, and inputting the text into the dialogue function.

For example, the behavior determination unit 236 obtains text representing the state of the robot 100 from the emotion of the robot 100 determined by the robot emotion determination unit 232, using an emotion table such as that shown in FIG. 7. Here, in the emotion table, an index number is assigned to each emotion value for each type of emotion, and text representing the state of the robot 100 is stored for each index number.

When the emotion of the robot 100 determined by the robot emotion determination unit 232 corresponds to index number "2", the text "very happy state" is obtained. Note that when the emotion of the robot 100 corresponds to multiple index numbers, multiple pieces of text representing the state of the robot 100 are obtained.

Also, for the emotions of the user 10, an emotion table such as that shown in FIG. 8 is prepared. Here, if the user's behavior is "Speaks good mood", the emotion of the robot 100 is index number "2", and the emotion of the user 10 is index number "3", then the following is input to the chat engine: "The robot is in a very happy state. The user is in a normal happy state. The user spoke to the user saying 'good mood'. How would you respond as the robot?" and the robot's behavior content is obtained. The behavior decision unit 236 decides the robot's behavior from this behavior content.

In this way, the robot 100 can change its behavior according to the index number that corresponds to the robot's emotions, so that the user gets the impression that the robot has a heart, and is encouraged to take actions such as talking to the robot.

Furthermore, the behavior decision unit 236 may generate the robot's behavior content by adding not only text representing the user's behavior, the user's emotions, and the robot's emotions, but also text representing the contents of the history data 222, adding a fixed sentence for asking about the robot's behavior content corresponding to the user's behavior, and inputting the result into a sentence generation model with a dialogue function. This allows the robot 100 to change its behavior according to the history data representing the user's emotions and behavior, so that the user has the impression that the robot has a personality, and is encouraged to take actions such as talking to the robot. The history data may also further include the robot's emotions and actions.

(Example)
Next, an example of the case where the robot 100 of this embodiment is applied to a child-rearing robot will be described. The robot 100 as a child-rearing robot is implemented, for example, in a personal computer, a tablet terminal, or a smartphone. It may also be implemented in a humanoid robot or a stuffed animal robot. The child-rearing robot suggests what to play with to the user, who is a toddler (or an infant), and further monitors the user's condition while playing and the surrounding environment, while watching over the user to see if there is anything unusual or dangerous about the user, and talks to the user, converses with the user, or plays with the user as appropriate.

The sensor unit 200 of the robot 100 includes, for example, a camera, a microphone, a non-contact biosensor, and a thermometer for interacting with the user and monitoring the user's condition. The camera acquires video of the user and video of the user's surroundings. The microphone collects the user's voice and surrounding sounds. The non-contact biosensor acquires biometric information such as the user's body temperature and heart rate. The thermometer acquires the temperature around the user. In addition, the control object 252 of the robot 100 includes a display and a speaker for interacting with the user.

The user emotion determination unit 231 of the robot 100 is equipped with an emotion recognition engine 231a capable of analyzing the user's emotion from the user's facial expression and voice and determining an emotion value. By utilizing the emotion recognition engine 231a, the robot 100 is able to accurately grasp the user's emotion (e.g., "I'm hungry," "I'm sleepy," "I want to play more," etc.) in real time. Because the user is a small child, it is difficult to determine the actual emotion from the content of the dialogue with the robot 100 alone. Therefore, it is desirable to analyze the emotion of the user 10 based on various information such as facial expression and tone of voice, and determine the behavior of the robot 100.

The storage unit 220 of the robot 100 stores advice information regarding how to respond to the user. Examples of advice information include, but are not limited to, what facial expression the user should have and what behavior the user should exhibit when they are sleepy, and what facial expression the user should have and what behavior the user should exhibit when they are hungry.

Next, the operation of the robot 100 will be described with reference to the flowchart in FIG. 9. Through the following steps S1 to S5-1 and S5-2, the robot 100 monitors the user's condition and converses with the user according to the user's preferences, situation, and reactions. It is assumed that the robot 100 is implemented in a personal computer.

(Step S1)
The robot 100 acquires the state of the user 10, the emotional value of the user 10, the emotional value of the robot 100, and the history data 222. Specifically, the robot 100 performs the same processes as steps S100 to S103 described above, and acquires the state of the user 10, the emotional value of the user 10, the emotional value of the robot 100, and the history data 222 based on the information acquired from the sensor unit 200.

(Step S2)
The robot 100 acquires the preferences of the user 10. Specifically, the behavior decision unit 236 decides, for example, to make an utterance asking the user 10 what he or she wants to play as the behavior of the robot 100, and the behavior control unit 250 controls the control target 252 (speaker) to make an utterance to the user 10. The user state recognition unit 230 recognizes the preferences of the user 10 based on information analyzed by the sensor module unit 210 (for example, the user's response).

For example, the robot 100 may utter a question such as "What do you want to play?" to the user. The robot 100 may analyze the user's preferences not only from the content of the user's 10 response to the question (e.g., "I want to play with building blocks"), but also from the facial expression of the user 10 acquired from the camera and the tone of voice during the conversation. If the user 10 does not respond to the question from the robot 100 or if the robot 100 detects that the user 10 is confused, the robot 100 may ask the user again by changing the wording of the question, such as by giving the user a choice such as "Do you want to play with building blocks? Or make a puzzle?". The robot 100 may also understand the user's preferences (e.g., wanting to sleep rather than play, wanting to eat a snack, etc.) from the user's emotions acquired by the emotion recognition engine 231a (e.g., "I'm sleepy," "I'm hungry," etc.).

(Step S3)
The robot 100 determines the content of the play to be proposed to the user 10. Specifically, the behavior decision unit 236 adds a fixed sentence such as "What action do you recommend the user to take at this time?" to the text representing the play preferences of the user 10, the user's emotions, the emotions of the robot 100, and the content stored in the history data 222, and inputs the added fixed sentence into the sentence generation model to obtain the content of the recommended play-related action. At this time, by considering not only the play preferences of the user 10 but also the emotions of the user 10 (e.g., "sleepy") and the history data 222, it is possible to propose a play-related action suitable for the user 10. When the emotion of the user 10 is determined to be "sleepy", a suggestion such as "Shall we take a nap?" may be made instead of a suggestion about play.

Furthermore, by taking the emotions of the robot 100 into consideration, the user 10 can be made to feel that the robot 100 has emotions. The emotion value of the robot 100 can be determined based on an emotion map as exemplified in FIG. 5. The emotion of the robot may vary depending on the surrounding circumstances, the time of day, and the state and emotions of the user. Specifically, for example, if the user handles a toy roughly or throws a toy at the robot 100, the emotion of the robot 100 may become negative, and the robot 100 may make utterances such as "Take good care of your toys," or "Don't throw them," to make the user change his or her behavior. In addition, if the user 10 does not change his or her behavior even after the robot 100 warns the user, the robot 100 may change the tone of its voice and make an angry utterance to make the user 10 change his or her behavior. In addition, if the user 10 seems to be absorbed in playing, the emotion of the robot 100 may become positive, and if the user 10 seems bored, the robot 100 may become negative, so that the robot 100 may be in tune with the emotion of the user. Furthermore, if it is late at night or if the user 10 receives a warning from a parent (such as "Go to bed early"), the robot 100 may have a negative emotion. In this way, the robot's emotion changes according to the attitude and feelings of the user 10, allowing the user 10 to feel as if they are being played with by someone with emotions.

Furthermore, the robot 100 may acquire history data 222 and, for example, if there is history data of when the user 10 played with building blocks in the past, refer to the details of the actions taken at that time. The history data may include information such as the date and time of day when the user played with the building blocks, the amount of time played, and the details of the play. It may also include information on the state of the user 10 at that time (e.g., whether they seemed to be having fun, bored, etc.).

(Step S4)
The robot 100 proposes the determined action content to the user and acquires the user's reaction. Specifically, the action control unit 250 controls the control target 252 (speaker) to make an utterance proposing to the user, such as "Shall we build a house with building blocks?" The user state recognition unit 230 recognizes the state of the user based on the information analyzed by the sensor module unit 210, and the user emotion determination unit 231 determines an emotion value indicating the emotion of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user recognized by the user state recognition unit 230. The action determination unit 236 judges whether the reaction of the user 10 is positive or not based on the state of the user 10 recognized by the user state recognition unit 230 and the emotion value indicating the emotion of the user 10, and determines whether to propose another action to the user 10 as the action of the robot 100.

(Step S5-1)
If the user's reaction to the suggestion is positive, the robot 100 confirms the action content. Whether the user's reaction is positive or not may be determined from the user's state (the content of the user's reply, facial expression, tone of voice, etc.) recognized by the user state recognition unit 230 or the user's emotion. When it is determined that the action content is to be confirmed as the action of the robot 100, the robot 100 may, under the control of the action control unit 250, make an utterance to encourage the user 10 to execute the proposed content, such as "I wonder what kind of house we can make."

(Step S5-2)
If the user 10's reaction is negative, the robot 100 determines another action to be proposed to the user. Whether the user's reaction is negative may be determined from the user's state (the content of the user's reply, facial expression, tone of voice, etc.) recognized by the user state recognition unit 230 or the user's emotion. When it is determined that another action is to be proposed to the user 10 as the action of the robot 100, the action decision unit 236 adds a fixed sentence, "Are there any other actions recommended for the user?" to the text representing the user's 10 preferences for playing with building blocks, the user's emotion, the robot 100's emotion, and the content stored in the history data 222, inputs the fixed sentence into the sentence generation model, and obtains the recommended content related to play. Then, the process returns to step S4, and the process of steps S4 to S5-2 is repeated until a positive reaction is obtained from the user and the proposed action is confirmed.

As described above, the robot 100 can make appropriate suggestions regarding play based on the user's preferences, the situation of the user 10, and the user's reactions. Furthermore, the content of the suggestions is also influenced by the emotions of the robot 100 itself, so the user 10 can have the feeling that they are being played with by someone who has emotions.

Once the action content related to play has been determined and the user 10 has begun playing with the building blocks as instructed by the robot 100, the robot 100 may be configured to appropriately praise or encourage the user 10 while watching the user's condition. For example, if the user 10 is stacking the blocks skillfully, the robot 100 may utter utterances such as "Almost there!" or "You're doing great!". On the other hand, if the blocks fall apart, the robot 100 may encourage the user by uttering utterances such as "Shall we try again?".

In addition, if a change in the user's emotions is detected while playing, an action may be taken in response to the change. For example, if the user's emotion is recognized as "sleepy," the robot may say something like, "Shall we take a nap?".

In addition, when a change in the robot 100's own emotions is detected, the robot 100 may make speech in accordance with the change in its emotions. For example, when it is late at night and the robot 100's emotions have become negative, the robot 100 may make a suggestion to end the game with building blocks, such as "Shall we tidy up now?".

In addition, if the robot detects an abnormality such as the room temperature being too high or too low while the user is playing, the robot 100 may notify the user's parents or change the temperature setting of the air conditioner in the room.

In the above example, the user is playing with building blocks, but the robot 100 can converse and make suggestions in accordance with the user's preferences and feelings in various situations.

For example, if the user is playing with a puzzle and seems to be having trouble figuring out the correct position to put a piece in, the robot 100 may provide speech to help (such as "Maybe it's a little lower"). On the other hand, if the user seems engrossed in playing, the robot 100 may simply watch over the user without speaking to them.

For example, when the user is watching a children's program, the robot 100 may ask the user a question such as "Do you like this character?" while observing the user's reaction, or the robot 100 itself may convey its favorite character based on its own emotions.

In the above example, the robot 100 is implemented in a personal computer, but it may also be implemented in a humanoid robot or a stuffed toy robot. Alternatively, it may be applied to a control device that is connected wirelessly or by wire to a controlled device (speaker or camera) mounted on a humanoid robot or a stuffed toy robot.

In the case of a humanoid robot, the humanoid robot may not only talk to the user, but also play with the user 10. For example, if the user is unable to stack blocks properly, the robot 100 may stack them for the user.

In the case of a stuffed toy robot, it may also converse with the user 10, suggest play activities in the same manner as above, and watch over the user. Furthermore, in the case of a stuffed toy robot, it may be placed next to the user when the user sleeps, or the user may sleep while holding it, so it may sing a lullaby or tell a story when the user is trying to fall asleep. At this time, it may talk to the user, saying things like, "It was fun playing with the building blocks today," based on the user's history data for that day.

The present invention has been described above using an embodiment, but the technical scope of the present invention is not limited to the scope described in the above embodiment. It is clear to those skilled in the art that various modifications and improvements can be made to the above embodiment. It is clear from the claims that forms with such modifications or improvements can also be included in the technical scope of the present invention.

The order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before" or "prior to," and it should be noted that the processes may be performed in any order, unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, it does not mean that it is necessary to perform the processes in that order.

5 System, 10, 11, 12 User, 20 Communication network, 100, 101, 102 Robot, 200 Sensor unit, 201 Microphone, 202 Depth sensor, 203 Camera, 204 Distance sensor, 210 Sensor module unit, 211 Voice emotion recognition unit, 212 Speech understanding unit, 213 Facial expression recognition unit, 214 Face recognition unit, 220 Storage unit, 221 Response rules, 222 History data, 230 User state recognition unit, 232 Emotion determination unit, 234 Behavior recognition unit, 236 Behavior determination unit, 238 Memory control unit, 250 Behavior control unit, 252 Control target, 280 Communication processing unit, 300 Server, 1200 Computer, 1210 Host controller, 1212 CPU, 1214 RAM, 1216 Graphic controller, 1218 Display device, 1220 I/O controller, 1222 communication interface, 1224 storage device, 1226 DVD drive, 1227 DVD-ROM, 1230 ROM, 1240 I/O chip

Claims (5)

A behavior control system for a robot applied to support childcare, comprising:
a user emotion determination unit that determines an emotion value indicating an emotion of a user who is a child-rearing subject;
a robot emotion determination unit for determining an emotion value indicating an emotion of the robot;
a behavior determination unit that determines a behavior of the robot based on at least one of the emotion value of the user and the emotion value of the robot, and information acquired by a sentence generation model having a dialogue function for allowing the user and the robot to converse with each other;
The action determination unit is
A behavior control system that determines the content of play that the robot will suggest to the user who is the subject of childcare. A user state recognition unit that recognizes the state of the user to whom the content of the play is proposed,
The action determination unit is
2. The behavior control system according to claim 1, wherein when it is determined that the user's reaction is negative based on the recognized state of the user and the emotional value of the user determined by the user emotion determination unit, the content of another play is determined based on at least one of the emotional value of the user and the emotional value of the robot, and information acquired by the sentence generation model. and a behavior control unit that executes the play when the user's reaction is determined to be positive.
The action determination unit is
3. The behavior control system according to claim 2, wherein, when a change in the user's state or the robot's emotion is detected while the play is being performed, a different behavior content is determined based on the change in emotion. The behavior control system according to claim 1, wherein the robot emotion determination unit determines the emotion value of the robot based on the attitude of the user. The behavior control system according to any one of claims 1 to 4, wherein the robot is mounted on a stuffed toy or is connected wirelessly or by wire to a control target device mounted on the stuffed toy.

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