WO2018000261A1

WO2018000261A1 - Method and system for generating robot interaction content, and robot

Info

Publication number: WO2018000261A1
Application number: PCT/CN2016/087740
Authority: WO
Inventors: 王昊奋; 邱楠; 杨新宇
Original assignee: 深圳狗尾草智能科技有限公司
Priority date: 2016-06-29
Filing date: 2016-06-29
Publication date: 2018-01-04
Also published as: CN106537293A

Abstract

Disclosed is a method for generating robot interaction content, comprising: proactively activating a robot (S101); obtaining user multimode information (S102); determining a user intention according to the user multimode information (S103); obtaining place scene information (S104); and generating robot interaction content by combining the user multimode information, the user intention, the place scene information, and a current robot life timeline (S105). The life timeline of a robot is added to generation of the robot interaction content, such that the robot is more humanized when interacting with human and has a human lifestyle within the life timeline. By the method, the humanization of robot interaction content generation, the human-robot interaction experience, and the intelligence can be improved.

Description

Method, system and robot for generating robot interactive content

Technical field

The invention relates to the field of robot interaction technology, and in particular to a method, a system and a robot for generating robot interactive content.

Background technique

Usually, when humans interact with the computer, they will actively wake up the robot interaction. After the robot picks up the sound, they will start interacting and make expression feedback in the process. Humans will initiate dialogue after the meeting, and the person who hears the speech will go through the brain. After the analysis of the speaker's words and expressions, reasonable expression feedback is performed. For the robot, the interaction mode of the robot is usually started by picking up and making feedback on the expression, which makes the robot's interaction low. The intelligence is very low and the following problems are saved: the robot that is actively switched up is mainly used to say hello and set the language and expression for the user. In this case, the robot actually follows the pre-designed interaction of humans. The way to output the expression, which leads to the robot does not have anthropomorphic, can not be like humans, when you see the other party, will analyze the other party's expression, then actively ask the other party's way, and feedback the corresponding expression.

Therefore, how to propose a machine expression generation method for automatically detecting facial expressions by actively awakening can improve the anthropomorphicity of robot interaction content generation, which is a technical problem that needs to be solved in the technical field.

Summary of the invention

The object of the present invention is to provide a method, a system and a robot for generating interactive content of a robot, and a method for generating a machine interactive content for automatically detecting a facial expression by actively awakening the robot, which can improve the anthropomorphicity of the robot interactive content generation and enhance human-computer interaction. Experience and improve intelligence.

The object of the present invention is achieved by the following technical solutions:

A method for generating robot interactive content, comprising:

Actively wake up the robot;

Obtain user multimodal information;

Determining a user's intention according to the user multimodal information;

Obtain location scene information;

The robot interaction content is generated according to the current multi-modality information, the user intention and the location scene information, in combination with the current robot life time axis.

Preferably, the step of actively waking up the robot comprises:

Obtain user multimodal information;

Matching with the multimodal information of the user according to a preset wakeup parameter;

If the user multi-modal information reaches the preset wake-up parameter, the robot will wake up actively.

Preferably, the method for generating parameters of the life time axis of the robot includes:

Extend the robot's self-awareness;

Get the parameters of the life timeline;

The self-cognitive parameters of the robot are fitted to the parameters in the life time axis to generate a robot life time axis.

Preferably, the step of expanding the self-cognition of the robot specifically comprises: combining the life scene with the self-knowledge of the robot to form a self-cognitive curve based on the life time axis.

Preferably, the step of fitting the self-cognitive parameter of the robot to the parameter in the life time axis comprises: using a probability algorithm to calculate each parameter of the robot on the life time axis after the time axis scene parameter is changed. The probability of change forms a fitted curve.

Preferably, wherein the life time axis refers to a time axis including 24 hours a day, and the parameters in the life time axis include at least a daily life behavior performed by the user on the life time axis and parameter values representing the behavior.

Preferably, the method further comprises: acquiring and analyzing a voice signal;

The generating the robot interaction content according to the user multimodal information and the user intention, combined with the current robot life time axis further includes:

According to the user multimodal information, the voice signal and the user intention, the robot interaction content is generated in combination with the current robot life time axis.

Preferably, the step of acquiring location scene information specifically includes: acquiring location scene information by using video information.

Preferably, the step of acquiring location scene information specifically includes: acquiring location scene information by using picture information.

Preferably, the step of acquiring location scene information specifically includes: acquiring location scene information by using gesture information.

The invention discloses a system for generating robot interactive content, comprising:

Light sensing automatic detection module for actively waking up the robot;

An expression analysis cloud processing module for acquiring user multimodal information;

An intent identification module, configured to determine a user intent according to the user multimodal information;

a scene recognition module, configured to acquire location scene information;

The content generating module is configured to generate the robot interaction content according to the current robot life time axis according to the user multimodal information and the user intention.

Preferably, the light sensing automatic detecting module is specifically configured to:

Obtain user multimodal information;

Preferably, the system comprises a time axis based and artificial intelligence cloud processing module for:

Extend the robot's self-awareness;

Get the parameters of the life timeline;

Preferably, the time-based and artificial intelligence cloud processing module is further configured to combine a life scene with a self-awareness of the robot to form a self-cognitive curve based on a life time axis.

Preferably, the time axis-based and artificial intelligence cloud processing module is further configured to: use a probability algorithm to calculate a probability of each parameter change of the robot on the life time axis after the time axis scene parameter is changed, to form a fitting curve.

Preferably, the system further includes: a voice analysis cloud processing module, configured to acquire and analyze the voice signal;

The content generating module is further configured to: generate the robot interaction content according to the current robot life time axis according to the user multimodal information, the voice signal, and the user intention.

Preferably, the scene recognition module is specifically configured to acquire location scene information by using video information.

Preferably, the scene recognition module is specifically configured to acquire location scene information by using picture information.

Preferably, the scene recognition module is specifically configured to acquire location scene information by using gesture information.

The invention discloses a robot comprising a system for generating interactive content of a robot as described above.

Compared with the prior art, the present invention has the following advantages: the existing robot is generally based on the method of generating the interactive interactive content of the question and answer interactive robot in the solid scene, and cannot generate the robot more accurately based on the current scene. expression. A method for generating interactive content of a robot, comprising: actively waking up a robot; acquiring multimodal information of the user; determining user intent according to the multimodal information of the user; acquiring location scene information; and according to the multimodal information of the user, User intent and location scene information, combined with the current robot life timeline to generate robot interaction content. In this way, when the user is away from the specific position of the robot, the robot actively wakes up, and recognizes that the robot interaction content is more accurately generated according to the user's multi-modal information and intention, combined with the location scene information and the life time axis of the robot, thereby being more accurate. Anthropomorphic interaction and communication with people. For people, everyday life has a certain regularity. In order to make robots communicate with people more anthropomorphic, let the robots sleep, exercise, eat, dance, read books, eat, make up, etc. in 24 hours a day. Sleep and other actions. Therefore, the present invention adds the life time axis in which the robot is located to the interactive content generation of the robot, and makes the robot more humanized when interacting with the human, so that the robot has a human lifestyle in the life time axis, and the method can enhance the robot interaction content. Generate anthropomorphic, enhance the human-computer interaction experience and improve intelligence.

DRAWINGS

1 is a flowchart of a method for generating interactive content of a robot according to Embodiment 1 of the present invention;

2 is a schematic diagram of a system for generating interactive content of a robot according to a second embodiment of the present invention.

detailed description

Although the flowcharts describe various operations as a sequential process, many of the operations can be implemented in parallel, concurrently or concurrently. The order of the operations can be rearranged. Processing may be terminated when its operation is completed, but may also have additional steps not included in the figures. Processing can correspond to methods, functions, procedures, subroutines, subroutines, and the like.

Computer devices include user devices and network devices. The user equipment or the client includes but is not limited to a computer, a smart phone, a PDA, etc.; the network device includes but is not limited to a single network server, a server group composed of multiple network servers, or a cloud computing-based computer or network server. cloud. The computer device can be operated separately to implement the invention, and can also access the network and The present invention is implemented by interworking with other computer devices in the network. The network in which the computer device is located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.

The terms "first," "second," and the like may be used herein to describe the various elements, but the elements should not be limited by these terms, and the terms are used only to distinguish one element from another. The term "and/or" used herein includes any and all combinations of one or more of the associated listed items. When a unit is referred to as being "connected" or "coupled" to another unit, it can be directly connected or coupled to the other unit, or an intermediate unit can be present.

The terminology used herein is for the purpose of describing the particular embodiments, The singular forms "a", "an", It is also to be understood that the terms "comprising" and """ Other features, integers, steps, operations, units, components, and/or combinations thereof.

The invention will now be further described with reference to the drawings and preferred embodiments.

Embodiment 1

As shown in FIG. 1 , a method for generating interactive content of a robot is disclosed in this embodiment, including:

S101, actively waking up the robot;

S102. Acquire user multimodal information.

S103. Determine a user intent according to the user multimodal information.

S104. Acquire location scene information.

S105. Generate robot interaction content according to the current robot life timeline 300 according to the user multimodal information, the user intention, and the location scene information.

For the application scenario, the existing robot is generally based on the method of generating interactive interactive content of the question and answer interaction robot in the solid scene, and cannot generate the expression of the robot more accurately based on the current scene. A method for generating interactive content of a robot, comprising: actively waking up a robot; acquiring multimodal information of the user; determining user intent according to the multimodal information of the user; acquiring location scene information; and according to the multimodal information of the user, User intent and location scene information, combined with the current robot life timeline to generate robot interaction content. In this way, when the user is away from the specific position of the robot, the robot actively wakes up, and recognizes that the robot interaction content is more accurately generated according to the user's multi-modal information and intention, combined with the location scene information and the life time axis of the robot, thereby being more accurate. Anthropomorphic interaction and communication with people. For people, everyday The life has a certain regularity. In order to make the robot more humanized when communicating with people, the robot will also have the functions of sleeping, exercising, eating, dancing, reading, eating, makeup, sleeping, etc. in 24 hours a day. Therefore, the present invention adds the life time axis in which the robot is located to the interactive content generation of the robot, and makes the robot more humanized when interacting with the human, so that the robot has a human lifestyle in the life time axis, and the method can enhance the robot interaction content. Generate anthropomorphic, enhance the human-computer interaction experience and improve intelligence. The robot life timeline 300 is completed and set in advance. Specifically, the robot life timeline 300 is a series of parameter collections, and this parameter is transmitted to the system to generate interactive content.

The multimodal information in this embodiment may be one of user expression, voice information, gesture information, scene information, image information, video information, face information, pupil iris information, light sense information, and fingerprint information. Several. In this embodiment, the user's expression is preferred, so that the recognition is accurate and the recognition efficiency is high.

In this embodiment, the life time axis is specifically: according to the time axis of human daily life, the robot is fitted with the time axis of human daily life, and the behavior of the robot follows the fitting action, that is, the robot of the day is obtained. Behavior, which allows the robot to perform its own behavior based on the life time axis, such as generating interactive content and communicating with humans. If the robot is always awake, it will act according to the behavior on this timeline, and the robot's self-awareness will be changed according to this timeline. The life timeline and variable parameters can be used to change the attributes of self-cognition, such as mood values, fatigue values, etc., and can also automatically add new self-awareness information, such as no previous anger value, based on the life time axis and The scene of the variable factor will automatically add to the self-cognition of the robot based on the scene that previously simulated the human self-cognition.

For example, when the user is not in front of the robot, the robot's light-sensing automatic detection module does not trigger, so the robot is in a sleep state. When the user walks in front of the robot, the robot's light-sensing automatic detection module detects the user's proximity, so the robot will actively wake up and recognize the user's expression, combined with the location scene information, the robot's life time axis, for example, current The time is 6 pm, the location scene is the doorway, the user's off-duty time, the user just returned home, then when the robot recognizes that the user's expression is happy, the active wake-up call, with a happy expression, when unhappy, take the initiative The song, with a sympathetic expression. If the current time is 9:00 am and the local scene is a room, then when the robot recognizes that the user's expression is happy, he actively wakes up and greets, with a good morning expression, and when unhappy, actively puts a song, and With a poor expression. The interactive content can be an expression or text or voice.

According to one of the examples, the step of actively waking up the robot includes:

Obtain user multimodal information;

In this way, the user multi-modal information, for example, the user's motion, the user's expression, etc., can be compared with the preset wake-up parameters after the acquisition, and if the preset wake-up parameter is reached, the robot will be actively woken up if not reached. Will not wake up. For example, after a human close to a robot, the detection module of the robot detects the proximity of the human, and actively wakes up itself to interact with humans. Wake-up robots can also perform expressions, actions, or other dynamic behaviors made by humans. If humans are standing still, do not make expressions and movements, or are in a static state such as lying still, then they may not reach the preset. The wake-up parameters are thus not considered to wake the robot, and the robot does not actively wake itself up when it detects these behaviors.

According to one example, the method for generating parameters of the robot life time axis includes:

Extend the robot's self-awareness;

Get the parameters of the life timeline;

The self-cognitive parameters of the robot are fitted to the parameters in the life time axis to generate a robot life time axis. In this way, the life time axis is added to the self-cognition of the robot itself, so that the robot has an anthropomorphic life. For example, add the cognition of lunch to the robot.

According to another example, the step of expanding the self-cognition of the robot specifically includes: combining the life scene with the self-awareness of the robot to form a self-cognitive curve based on the life time axis. In this way, the life time axis can be specifically added to the parameters of the robot itself.

According to another example, the step of fitting the parameter of the self-cognition of the robot to the parameter in the life time axis comprises: using a probability algorithm to calculate the time of the robot on the life time axis after the time axis scene parameter is changed The probability of each parameter change forms a fitted curve. In this way, the parameters of the robot's self-cognition can be specifically matched with the parameters in the life time axis.

For example, in 24 hours a day, the robot will have sleep, exercise, eat, dance, read books, eat, make up, sleep and other actions. Each action will affect the self-cognition of the robot itself, and combine the parameters on the life time axis with the self-cognition of the robot itself. After fitting, the robot's self-cognition includes, mood, fatigue value, intimacy. , goodness, number of interactions, three-dimensional cognition of the robot, age, height, weight, intimacy, game scene value, game object value, location scene value, location object value, etc. For the robot to identify the location of the scene, such as cafes, bedrooms, etc.

The machine will perform different actions in the time axis of the day, such as sleeping at night, eating at noon, exercising during the day, etc. All the scenes in the life time axis will have an impact on self-awareness. These numerical changes are modeled by the dynamic fit of the probability model, fitting the probability that all of these actions occur on the time axis. Scene Recognition: This type of scene recognition changes the value of the geographic scene in self-cognition.

According to another example, the method further comprises: acquiring and analyzing a speech signal;

According to the user multimodal information, the voice signal and the user intention, the robot interaction content is generated in combination with the current robot life time axis. In this way, the robot interactive content can be generated in combination with the voice signal, which is more accurate.

According to another example, the step of acquiring location scene information specifically includes: acquiring location scene information by using video information. Such location scene information can be obtained through video, and the video acquisition is more accurate.

According to another example, the step of acquiring location scene information specifically includes: acquiring location scene information by using picture information. The image acquisition can save the robot's calculations and make the robot's reaction more rapid.

According to another example, the step of acquiring location scene information specifically includes: acquiring location scene information by using gesture information. The gesture can be used to make the robot more applicable. For example, if the disabled or the owner sometimes does not want to talk, the gesture can be used to transmit information to the robot.

Embodiment 2

As shown in FIG. 2, in this embodiment, a system for generating interactive content of a robot includes:

The light sensing automatic detecting module 201 is configured to actively wake up the robot;

The expression analysis cloud processing module 202 is configured to acquire user multimodal information;

The intent identification module 203 is configured to determine a user intent according to the user multimodal information;

a scene recognition module 204, configured to acquire location scene information;

The content generation module 205 is configured to generate the robot interaction content according to the current robot life time axis sent by the robot life timeline module 301 according to the user multimodal information, the user intention, and the location scene information.

This allows the robot to actively wake up when the user is at a specific location from the robot, and It is recognized that the robot interaction content is more accurately generated according to the user multi-modal information and intention, combined with the location scene information and the life time axis of the robot, thereby more accurately and anthropomorphic interaction and communication with the person. For people, everyday life has a certain regularity. In order to make robots communicate with people more anthropomorphic, let the robots sleep, exercise, eat, dance, read books, eat, make up, etc. in 24 hours a day. Sleep and other actions. Therefore, the present invention adds the life time axis in which the robot is located to the interactive content generation of the robot, and makes the robot more humanized when interacting with the human, so that the robot has a human lifestyle in the life time axis, and the method can enhance the robot interaction content. Generate anthropomorphic, enhance the human-computer interaction experience and improve intelligence.

For example, when the user is not in front of the robot, the robot's light-sensing automatic detection module does not trigger, so the robot is in a sleep state. When the user walks in front of the robot, the robot's light-sensing automatic detection module detects the user's proximity, so the robot will actively wake up and recognize the user's expression, combined with the location scene information, the robot's life time axis, for example, current The time is 6 pm, the location scene is the doorway, the user's off-duty time, the user just returned home, then when the robot recognizes that the user's expression is happy, the active wake-up call, with a happy expression, when unhappy, take the initiative The song, with a sympathetic expression.

According to one example, the light sensing automatic detecting module is specifically configured to:

Obtain user multimodal information;

In this way, the user multimodal information, for example, the user's motion, the user's expression, etc., can be compared with the preset wakeup parameters, and if the preset wakeup parameters are reached, the robot is actively awakened, for example, in humans. After approaching the robot, the robot's detection module detects the proximity of humans and actively wakes itself up to interact with humans. Wake-up robots can also perform expressions, actions, or other dynamic behaviors made by humans. If humans are standing still, do not make expressions and movements, or are in a static state such as lying still, then they may not reach the preset. The wake-up parameters are thus not considered to wake the robot, and the robot does not actively wake itself up when it detects these behaviors.

According to one example, the system includes a time axis based and artificial intelligence cloud processing module for:

Extend the robot's self-awareness;

Get the parameters of the life timeline;

Fitting the parameters of the robot's self-cognition with the parameters in the life time axis, generating machine The life axis of the person.

In this way, the life time axis is added to the self-cognition of the robot itself, so that the robot has an anthropomorphic life. For example, add the cognition of lunch to the robot.

According to another example, the time-based and artificial intelligence cloud processing module is further configured to combine a life scene with a self-awareness of the robot to form a self-cognitive curve based on a life time axis. In this way, the life time axis can be specifically added to the parameters of the robot itself.

According to another example, the time axis-based and artificial intelligence cloud processing module is further configured to: use a probability algorithm to calculate a probability of each parameter change of the robot on the life time axis after the time axis scene parameter changes, to form a fit curve. In this way, the parameters of the robot's self-cognition can be specifically matched with the parameters in the life time axis. Among them, the probability algorithm can adopt the Bayesian probability algorithm.

According to another example, the system further includes: a voice analysis cloud processing module, configured to acquire and analyze the voice signal;

The content generating module is further configured to: generate the robot interaction content according to the current robot life time axis according to the user multimodal information, the voice signal, and the user intention. In this way, the robot interactive content can be generated in combination with the voice signal, which is more accurate.

According to another example, the scene recognition module is specifically configured to acquire location scene information by using video information. Such location scene information can be obtained through video, and the video acquisition is more accurate.

According to another example, the scene recognition module is specifically configured to obtain Take location scene information. The image acquisition can save the robot's calculations and make the robot's reaction more rapid.

According to another example, the scene recognition module is specifically configured to acquire location scene information by using gesture information. The gesture can be used to make the robot more applicable. For example, if the disabled or the owner sometimes does not want to talk, the gesture can be used to transmit information to the robot.

In this embodiment, a robot is disclosed, including a robot interaction content generation system according to any of the above.

The above is a further detailed description of the present invention in connection with the specific preferred embodiments, and the specific embodiments of the present invention are not limited to the description. It will be apparent to those skilled in the art that the present invention may be made without departing from the spirit and scope of the invention.

Claims

A method for generating interactive content of a robot, comprising:

Actively wake up the robot;

Obtain user multimodal information;

Determining a user's intention according to the user multimodal information;

Obtain location scene information;

The robot interaction content is generated according to the current multi-modality information, the user intention and the location scene information, in combination with the current robot life time axis.
The generating method according to claim 1, wherein the step of actively waking up the robot comprises:

Obtain user multimodal information;

Matching with the multimodal information of the user according to a preset wakeup parameter;

If the user multi-modal information reaches the preset wake-up parameter, the robot will wake up actively.
The generating method according to claim 1, wherein the method further comprises: acquiring and analyzing a voice signal;

The generating the robot interaction content according to the user multimodal information and the user intention, combined with the current robot life time axis further includes:

According to the user multimodal information, the voice signal and the user intention, the robot interaction content is generated in combination with the current robot life time axis.
The generating method according to claim 1, wherein the generating method of the parameter of the life time axis of the robot comprises:

Extend the robot's self-awareness;

Get the parameters of the life timeline;

The self-cognitive parameters of the robot are fitted to the parameters in the life time axis to generate a robot life time axis.
The generating method according to claim 4, wherein the step of expanding the self-cognition of the robot comprises: combining the life scene with the self-awareness of the robot to form a self-cognitive curve based on the life time axis.
The generating method according to claim 4, wherein the step of fitting the parameters of the self-cognition of the robot to the parameters in the life time axis comprises: using a probability algorithm to calculate the robot on the life time axis The probability of each parameter change after the time axis scene parameter is changed forms a fitted curve.
The generating method according to claim 4, wherein the life time axis refers to a time axis including 24 hours a day, and the parameter in the life time axis includes at least a user performing on the life time axis. Daily life behavior and the values of the parameters that represent the behavior.
The generating method according to claim 1, wherein the step of acquiring location scene information comprises: acquiring location scene information by using video information.
The generating method according to claim 1, wherein the step of acquiring location scene information comprises: acquiring location scene information by using picture information.
The generating method according to claim 1, wherein the step of acquiring location scene information comprises: acquiring location scene information by using gesture information.
A system for generating interactive content of a robot, comprising:

Light sensing automatic detection module for actively waking up the robot;

An expression analysis cloud processing module for acquiring user multimodal information;

An intent identification module, configured to determine a user intent according to the user multimodal information;

a scene recognition module, configured to acquire location scene information;

The content generating module is configured to generate the robot interaction content according to the current user life time axis according to the user multimodal information, the user intent, and the location scene information.
The generating system according to claim 11, wherein the light sensing automatic detecting module is specifically configured to:

Obtain user multimodal information;

Matching with the multimodal information of the user according to a preset wakeup parameter;

If the user multi-modal information reaches the preset wake-up parameter, the robot will wake up actively.
The generating system according to claim 11, wherein the system further comprises: a voice analysis cloud processing module, configured to acquire and analyze a voice signal;

The content generating module is further configured to: generate the robot interaction content according to the current robot life time axis according to the user multimodal information, the voice signal, and the user intention.
The generating system according to claim 11, wherein the system comprises a time axis based and artificial intelligence cloud processing module for:

Extend the robot's self-awareness;

Get the parameters of the life timeline;

The self-cognitive parameters of the robot are fitted to the parameters in the life time axis to generate a robot life time axis.
The generating system according to claim 14, wherein said time axis is based The artificial intelligence cloud processing module is further used to combine the life scene with the self-knowledge of the robot to form a self-cognitive curve based on the life time axis.
The generating system according to claim 14, wherein the time-based and artificial intelligence cloud processing module is further configured to: use a probability algorithm to calculate each of the robots on the life time axis after the time axis scene parameter changes The probability of a parameter change forms a fitted curve.
The generating system according to claim 14, wherein said life time axis refers to a time axis including 24 hours a day, and parameters in said life time axis include at least a user performing on said life time axis Daily life behavior and the values of the parameters that represent the behavior.
The generating system according to claim 11, wherein the scene recognition module is specifically configured to acquire location scene information by using video information.
The generating system according to claim 11, wherein the scene recognition module is specifically configured to acquire location scene information by using picture information.
The generating system according to claim 11, wherein the scene recognition module is specifically configured to acquire location scene information by using gesture information.
A robot characterized by comprising a robot interactive content generating system according to any one of claims 10 to 20.