WO2018000258A1

WO2018000258A1 - Method and system for generating robot interaction content, and robot

Info

Publication number: WO2018000258A1
Application number: PCT/CN2016/087736
Authority: WO
Inventors: 邱楠; 杨新宇; 王昊奋
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: CN106489114A

Abstract

A method for generating robot interaction content, comprising: obtaining user information, and determining a user intention according to the user information; obtaining place scene information; and generating robot interaction content by combining the user intention, the place scene information, and a current robot life timeline. 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, for the application scenario, the robot is generally based on the question and answer interaction in the solid scene, the life scene of the person on a certain time axis, such as eating, sleeping, exercising, etc., the changes of various life scene values will affect the human expression. The feedback, and for the scene in which it is located, will affect the changes in human expression, such as: excited in the billiard room, very happy at home. For the robot, at present, the robot wants to make the expression feedback, mainly through the pre-set method and depth learning, there is no better solution to the question and answer on the scene, which leads to the robot can not be more anthropomorphic, Can not be like humans, life scenes at different time points, location scenes, showing different expressions, that is, the way the robot interactive content is generated is completely passive, so the generation of expressions requires a lot of human-computer interaction, resulting in the intelligence of the robot Very poor sex.

Therefore, how to provide a method for generating interactive content of robots and improve the human-computer interaction experience has become an urgent technical problem.

Summary of the invention

The object of the present invention is to provide a method, a system and a robot for generating robot interactive content, so that the robot has a human lifestyle in the life time axis, and the method can improve the anthropomorphicity of the robot interaction content generation and enhance the human-computer interaction experience. Improve intelligence.

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

A method for generating robot interactive content, comprising:

Obtaining user information, and determining a user intention according to the user information;

Obtain location scene information;

According to the user intent and location scene information, the robot interaction content is generated in combination with the current robot life time axis.

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 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.

Preferably, the user information includes voice information, the step of acquiring user information, and determining the user's intention according to the user information specifically includes: acquiring voice information, and determining a user intention according to the voice information.

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

An intention identification module, configured to acquire user information, and determine a user intention according to the user 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 intention and the location scene 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 specifically 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-based and artificial intelligence cloud processing module is specifically 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 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 gesture information. The location scene information is obtained by the gesture information.

Preferably, the user information includes voice information, and the intent identification module is specifically configured to: acquire voice information, and determine a user intention according to the voice 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 fixed scene, and cannot generate the robot more accurately based on the current scene. Interactive content. The generating method of the present invention includes: acquiring user information, determining user intent according to the user information; acquiring location scene information; and generating robot interaction content according to the current user life time axis according to the user intention and location scene information. In this way, according to the current location scene information, combined with the life time axis of the robot, the robot interaction content can be more accurately generated, thereby more accurately and 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 operate alone to carry out the invention, and can also access the network and implement the invention through interoperation 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. Acquire user information, and determine a user intention according to the user information.

S102. Acquire location scene information.

S103. Generate robot interaction content according to the current user life timeline 300 according to the user intention and location scene information.

For the application scenario, the existing robot is generally based on the method of generating the interaction content of the question and answer interactive robot in the fixed scene, and cannot generate the interactive content of the robot more accurately based on the current scene. The generating method of the present invention includes: acquiring user information, determining user intent according to the user information; acquiring location scene information; and generating robot interaction content according to the current user life time axis according to the user intention and location scene information. In this way, according to the current location scene information, combined with the life time axis of the robot, the robot interaction content can be more accurately generated, thereby more accurately and 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. The interactive content can be an expression or text or voice. 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 user information in this embodiment may be one or more of user expression, voice information, gesture information, scene information, image information, video information, face information, pupil iris information, light sense information, and fingerprint information. . 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, according to the human way, the self-cognition value of the robot itself in the time axis of daily life is fitted, and the behavior of the robot is according to this The action is to get the robot's own behavior in one day, so that the robot can 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, the user speaks to the robot: “It’s so sleepy”, the robot understands that the user is very sleepy, and then combines the collected scene scene information into the room, and the robot life timeline. For example, if the current time is 9:00 am, then the robot knows that the owner just got up, then he should ask the owner early, for example, answer "good morning" as a reply, or with an expression, a picture, etc., the interactive content in the present invention. Can be understood as the response of the robot. And if the user speaks to the robot: "It's so sleepy", the robot understands that the user is very sleepy, and then combines the collected scene scene information into the room, and the robot life time axis, for example, the current time is 9:00 pm Then, the robot knows that the owner needs to sleep, then he will reply to the words "master good night, sleep well" and other similar terms, and can also be accompanied by expressions, pictures and so on. This kind of approach is more anthropomorphic than simply relying on scene recognition to generate replies and expressions that are more intimate with people's lives.

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;

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. The probability algorithm may be a Bayesian probability algorithm.

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 and eating at noon. Daytime sports, etc., all of these scenes in the life timeline 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 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.

According to another example, the user information includes voice information, the step of acquiring user information, and determining the user's intention according to the user information specifically includes: acquiring voice information, and determining a user intention according to the voice information. In this way, the user's voice can be used to obtain the user's intention, so that the robot grasps the user's intention more accurately. Of course, in this embodiment, other methods such as text input may be used to let the robot know the intention of the user.

In more specific applications, the following is a detailed explanation of the self-cognition of the robot itself through the scene of the robot on the life time axis, such as the normal life scene within a day, eating, sleeping, exercising, these life scenes It will affect the mood and fatigue value of the robot itself. We will fit these effects to form a self-cognitive curve based on the time axis. By fitting the parameters in self-cognition with the parameters of the scene used in the life timeline, we can use the Bayesian probability algorithm to estimate the parameters between the robots using the Bayesian network to calculate the probability on the life time axis. After the time axis parameters of the robot itself change, the probability of each parameter change forms a fitting curve, which dynamically affects the self-cognition of the robot itself. The life time axis makes regular changes to the robot itself during the time period. The change comes from the fitting of the self-cognition in the life scene in the previous algorithm, which produces the influence of personification. At the same time, coupled with the identification of the location scene, the robot will know its geographical location, and will change the way the interactive content is generated according to the geographical environment in which it is located. Geographical changes are based on our geographic scene recognition algorithms that allow robots to know where they are located, such as cafes or bedrooms. In addition, this innovative module makes the robot itself have a human lifestyle. For the expression, it can be changed according to the scene in the ground.

Embodiment 2

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

The intent identification module 201 is configured to acquire user information, and determine a user intention according to the user information;

The scene recognition module 202 is configured to acquire location scene information.

The content generation module 203 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 intention and the location scene information.

In this way, according to the current location scene information, combined with the life time axis of the robot, the robot interaction content can be more accurately generated, thereby more accurately and 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.

For example, the user speaks to the robot: “It’s so sleepy”, the robot understands that the user is very sleepy, and then combines the collected scene scene information into the room, and the robot life timeline, for example, the current time is 9:00 am. Then, the robot knows that the owner just got up, then he should ask the owner early, for example, answer "good morning" as a reply, and can also be accompanied by expressions, pictures, etc. The interactive content in the present invention can be understood as the reply of the robot. And if the user speaks to the robot: "It's so sleepy", the robot understands that the user is very sleepy, and then combines the collected scene scene information into the room, and the robot life time axis, for example, the current time is 9:00 pm Then, the robot knows that the owner needs to sleep, then he will reply to the words "master good night, sleep well" and other similar terms, and can also be accompanied by expressions, pictures and so on. This kind of approach is more anthropomorphic than simply relying on scene recognition to generate replies and expressions that are more intimate with people's lives.

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;

According to another example, the time-based and artificial intelligence cloud processing module is specifically 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-based and artificial intelligence cloud processing module is specifically configured to: use a probability algorithm to calculate a probability of each parameter change of a robot on a life time axis after a change of a time axis scene parameter, 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. The probability algorithm may be a Bayesian probability algorithm.

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 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 acquire 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 scene recognition module is specifically configured to obtain by using gesture information. Take location scene 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.

According to another example, the user information includes voice information, and the intent recognition module is specifically configured to: acquire voice information, and determine a user intention according to the voice information. In this way, the user's voice can be used to obtain the user's intention, so that the robot grasps the user's intention more accurately. Of course, in this embodiment, other methods such as text input may be used to let the robot know the intention of the user.

In addition, in this embodiment, a robot is further 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:

Obtaining user information, and determining a user intention according to the user information;

Obtain location scene information;

According to the user intent and location scene information, 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 2, wherein the step of expanding the self-cognition of the robot specifically 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 2, wherein 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 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 1, wherein the living time axis refers to a time axis including 24 hours a day, and the parameter in the living time axis includes at least a user performing on the living 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.
The generating method according to claim 1, wherein the user information comprises voice information, the step of acquiring user information, and determining the user's intention according to the user information comprises: acquiring voice information according to the voice information. Determine user intent.
A system for generating interactive content of a robot, comprising:

An intention identification module, configured to acquire user information, and determine a user intention according to the user 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 intention and the location scene information.
The generating system according to claim 10, 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 11, wherein the time-based and artificial intelligence cloud processing module is specifically 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.
The generating system according to claim 11, wherein the time-based and artificial intelligence cloud processing module is specifically 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 10, 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 10, wherein the scene recognition module is specifically configured to acquire location scene information by using video information.
The generating system according to claim 10, wherein the scene recognition module is specifically configured to acquire location scene information by using picture information.
The generating system according to claim 10, wherein the scene recognition module is specifically configured to acquire location scene information by using gesture information.
The generating system according to claim 10, wherein the user information comprises voice information, and the intent recognition module is specifically configured to: acquire voice information, and determine a user intent according to the voice information.
A robot comprising a robot interactive content generating system according to any one of claims 10 to 18.