User presentation data-based task configuration method and related device

Abstract

The invention discloses an own task configuration method based on user demonstration data and a related device. The method comprises the following steps: sending a busy / idle query request to a server in response to a first operation of a user; a busy / idle query reply message from the server is received, a task configuration interface is displayed, and the task configuration interface comprises a plurality of task selection controls and a plurality of human-shaped robot selection controls; sending a task configuration message to the server in response to a second operation of the user, wherein the task configuration message carries the target humanoid robot and the target task; a training result message from the server is received, a training result interface is displayed, the training result interface is used for displaying the training score of the target humanoid robot executing the target control scheme, the target control scheme is the content generated by the server according to the user demonstration data, and the user demonstration data is used for representing the action executed by the user for the target task. According to the invention, remote training of the robot can be realized, and the diversity and flexibility of task execution of the humanoid robot are improved.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to an embodied task configuration method based on user demonstration data and related devices. Background Art

[0002] A humanoid robot, also known as a humanoid robot, is a robot with a humanoid shape. Before a humanoid robot is put into use, it needs to be continuously trained and learned based on human actions. At present, in order to improve efficiency and save costs, manufacturers usually provide the same set of training data to multiple humanoid robots for training, so as to improve the functions of the humanoid robots and ship them out of the factory. Such training cannot achieve differentiated training for different humanoid robots, and the training effect is limited. Summary of the invention

[0003] The embodiments of the present application provide an embodied task configuration method and related devices based on user demonstration data, in order to realize the application function of remote training of humanoid robots, thereby improving the diversity and flexibility of humanoid robot training and learning.

[0004] In a first aspect, an embodiment of the present application provides an embodied task configuration method based on user demonstration data, which is applied to a user device of an embodied task configuration system, wherein the embodied task configuration system includes a server, a humanoid robot, the user device, and a virtual reality device, wherein the server is respectively connected to the humanoid robot, the user device, and the virtual reality device for communication, and the method includes: Sending a busy / idle query request to the server in response to a first operation performed by a user on the user device; receiving a busy / idle query reply message from the server, and displaying a task configuration interface, wherein the task configuration interface includes a plurality of task selection controls and a plurality of humanoid robot selection controls, wherein the task selection controls are used to display corresponding task contents, and the humanoid robot selection controls are used to display the busy / idle status of the corresponding humanoid robot; In response to a second operation performed by the user on the user device, a task configuration message is sent to the server, wherein the task configuration message carries a target humanoid robot and a target task, wherein the target humanoid robot is one of the humanoid robots corresponding to a plurality of humanoid robot selection controls, and the target task is one of the task contents corresponding to the plurality of task selection controls; Receive a training result message from the server and display a training result interface, wherein the training result interface is used to display the training score of the target humanoid robot executing the target control scheme, wherein the target control scheme is content generated by the server based on user demonstration data, wherein the user demonstration data is content uploaded to the server by the virtual reality device, and wherein the user demonstration data is used to characterize the actions performed by the user for the target task.

[0005] In a possible example of the first aspect, the task selection control includes a first task control and a second task control, the task content corresponding to the first task control is a task performed on an upper body of a humanoid robot, and the task content corresponding to the second task control is a task performed on a whole body of the humanoid robot; When the target task is the task content corresponding to the first task control, the user demonstration data includes user posture data, and the user posture data is operation data collected by the virtual reality device; When the target task is the task content corresponding to the second task control, the user demonstration data includes the user posture data and task video data, and the task video data is the video content of the user's whole body activities collected by the virtual reality device.

[0006] In a possible example of the first aspect, the target control solution is generated by the server according to the following steps: Determine a task type of the target task, where the task type includes a first task type and a second task type, where the first task type is a task type corresponding to a first task control, and the second task type is a task type corresponding to a second task control; If the task type of the target task is the first task type, determining an execution object according to the target task, and generating the target control scheme according to the execution object and the user posture data, the execution object including the left hand, the right hand or both hands; If the task type of the target task is the second task type, the task video data is analyzed to determine the user motion posture data, and the target control scheme is generated according to the user motion posture data and the user posture data.

[0007] In a possible example of the first aspect, before determining the task type of the target task, the step of generating the target control solution further includes: Determining a fluctuation value of the user posture data, wherein the fluctuation value is used to characterize a degree of stability of the user posture data; comparing the fluctuation value with a preset threshold; If the fluctuation value is greater than the preset threshold, the user posture data is smoothed to obtain target posture data, and the target posture data is used to generate the target control solution.

[0008] In a possible example of the first aspect, the training score is generated by the server according to the following steps: Acquire training data uploaded by the target humanoid robot, wherein the training data includes training time, preset training times, and training success rate; A training score is determined based on the training data.

[0009] In a possible example of the first aspect, the training result interface is further used to display a score result of points, where the points are used to exchange items according to a preset score rule; and the score result of the points is determined by the server according to the following steps: Get the preset basic score for a single training session; Determine a training score according to the training score and a preset scoring rule; The basic score and the training score are summed to obtain the score result.

[0010] In a possible example of the first aspect, after receiving the training result message from the server and displaying the training result interface, the method further includes: In response to a third operation performed by the user on the user device, a machine query request is sent to the server, where the machine query request is used to query a recommended humanoid robot suitable for the user; Receive a query reply message from a server and display a product recommendation interface, wherein the product recommendation interface is used to display the device information of the recommended humanoid robot, and the recommended humanoid robot is determined by the server according to the following steps: obtaining historical data of the user for the target task, wherein the historical data includes all training data of the user selecting different humanoid robots to perform the target task; determining the adaptation values ​​corresponding to all humanoid robots that perform the target task according to the historical data; comparing all the adaptation values, and determining that the humanoid robot corresponding to the largest adaptation value is the recommended humanoid robot.

[0011] In a second aspect, an embodiment of the present application provides an embodied task configuration device based on user demonstration data, which is applied to a user device of an embodied task configuration system, wherein the embodied task configuration system includes a server, a humanoid robot, the user device, and a virtual reality device, wherein the server is respectively connected to the humanoid robot, the user device, and the virtual reality device for communication, and the embodied task configuration device based on user demonstration data includes: A first response unit, configured to send a busy / idle query request to the server in response to a first operation performed by a user on the user equipment; A first receiving unit is used to receive a busy / idle query reply message from the server and display a task configuration interface, wherein the task configuration interface includes a plurality of task selection controls and a plurality of humanoid robot selection controls, wherein the task selection controls are used to display corresponding task contents, and the humanoid robot selection controls are used to display the busy / idle status of the corresponding humanoid robot; a second response unit, configured to send a task configuration message to the server in response to a second operation performed by the user on the user device, wherein the task configuration message carries a target humanoid robot and a target task, wherein the target humanoid robot is one of the humanoid robots corresponding to a plurality of humanoid robot selection controls, and the target task is one of the task contents corresponding to the plurality of task selection controls; A second receiving unit is used to receive a training result message from the server and display a training result interface, wherein the training result interface is used to display the training score of the target humanoid robot executing the target control scheme, wherein the target control scheme is content generated by the server based on user demonstration data, wherein the user demonstration data is content uploaded to the server by the virtual reality device, and wherein the user demonstration data is used to characterize the actions performed by the user for the target task.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute some or all of the steps described in the first aspect of this embodiment.

[0014] It can be seen that in this embodiment, the user device sends a busy / idle query request to the server in response to the first operation of the user acting on the user device; and receives a busy / idle query reply message from the server, and can display a task configuration interface, which includes multiple task selection controls and multiple humanoid robot selection controls, the task selection control is used to display the corresponding task content, and the humanoid robot selection control is used to display the busy / idle status of the corresponding humanoid robot. It is also possible to send a task configuration message to the server in response to the second operation of the user acting on the user device, the task configuration message carries a target humanoid robot and a target task, the target humanoid robot is one of the humanoid robots corresponding to the multiple humanoid robot selection controls, and the target task is one of the task contents corresponding to the multiple task selection controls; receive a training result message from the server, and display a training result interface, which is used to display the training score of the target humanoid robot executing the target control scheme, the target control scheme is the content generated by the server according to the user demonstration data, the user demonstration data is the content uploaded to the server by the virtual reality device, and the user demonstration data is used to characterize the actions performed by the user for the target task. It can be seen that in this application, the user can select the required humanoid robot selection control and task selection control in the task configuration interface displayed by the user device to determine the target humanoid robot to be trained and the target task for its corresponding training, and after the training is completed, the training result interface is displayed through the user device for the user to understand the training situation. In this way, the application function of remote training of humanoid robots can be realized, which is conducive to differentiated training of humanoid robots and improves the diversity and flexibility of humanoid robot training and learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the architecture of an embodied task configuration system provided in an embodiment of the present application; Figure 2 It is a flowchart of an embodied task configuration method based on user demonstration data provided in an embodiment of the present application; Figure 3 This is a schematic diagram of an interface for initiating a task provided in an embodiment of the present application; Figure 4 This is a schematic diagram of a task configuration interface provided in an embodiment of the present application; Figure 5This is a schematic diagram of a wear reminder interface provided in an embodiment of the present application; Figure 6 is a schematic diagram of a training result interface provided in an embodiment of the present application; Figure 7 This is a schematic diagram of a product recommendation interface provided in an embodiment of the present application; Figure 8 is a composition example diagram of an electronic device provided in an embodiment of the present application; Fig. 9 It is a functional unit composition block diagram of a first embodied task configuration device based on user demonstration data provided by an embodiment of the present application; Fig.10 It is a block diagram of the functional units of a second embodied task configuration device based on user demonstration data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] The embodiments of the present application are described below with reference to the accompanying drawings.

[0021] The technical solution of this application can be applied to Figure 1The embodied task configuration system 10 shown includes a server 110, a user device 120, a humanoid robot 130, and a virtual reality device 140. The server 110 is in communication connection with the user device 120, the humanoid robot 130, and the virtual reality device 140, respectively.

[0022] Among them, the server 110 can be an outsourced server, a cloud server, an edge server, an intelligent robot, etc., which is not limited here.

[0023] The user device 120 may be a terminal device configured with a software application corresponding to the humanoid robot training provided by the server 110, and the user device 120 may implement the relevant functions of the software application by communicating with the server 110. For example, the user device 120 may be a smart phone (such as an Android phone, an iOS phone, a Windows Phone phone, etc.), a smart computer (such as a tablet computer, a PDA, a laptop computer, a desktop computer, etc.), a driving recorder, a mobile Internet device (MID, Mobile Internet Devices) or a wearable device (such as a smart watch, a Bluetooth headset), etc. The above are only examples, not exhaustive, and include but are not limited to the above devices.

[0024] The virtual reality device 140 includes a head display device (i.e., a head-mounted stereo display, etc.) and an interactive device. The head display device is used to dynamically display the visual information of the target humanoid robot so that the user can operate from the perspective of the target humanoid robot. The interactive device is used to collect user demonstration data for the server to analyze and process to obtain the target control solution to control the training of the target humanoid robot. The interactive device includes wearable devices such as data gloves for collecting interactive data and motion capture devices (such as posture estimators such as RGB cameras).

[0025] See also Figure 2 , Figure 2 is a flow chart of a method for configuring an embodied task based on user demonstration data provided by an embodiment of the present application. The method can be applied in the following aspects: Figure 1 The user device 120 in the embodied task configuration system shown in FIG. Figure 2 As shown, the embodied task configuration method based on user demonstration data includes: Step S210: sending a busy / idle query request to the server in response to a first operation performed by the user on the user equipment.

[0026] The first operation refers to a selection operation performed by a user on a control for initiating a busy / idle query request in an interface displayed by a user device. The selection operation may be a single click, double click, long press, or the like. For example, see Figure 3 , users target Figure 3 The task initiation control 310 in the task initiation interface 30 shown in the figure performs a selection operation, and the user device can send a busy query request to the server. Specifically, the task initiation interface 30 can also include controls such as a mall and a training history, so that functions such as product purchase and training history query can be realized by triggering these controls.

[0027] The busy / idle query request is used to query the busy / idle status of the robot, which includes the busy state and the idle state. For example, see Figure 4 The busy state is used to indicate that the corresponding humanoid robot is currently performing training tasks for other users. The idle state is used to indicate that the corresponding humanoid robot is not currently selected by other users, and the user can select the humanoid robot to perform training tasks.

[0028] Step S220, receiving a busy / idle query reply message from the server, and displaying a task configuration interface, wherein the task configuration interface includes a plurality of task selection controls and a plurality of humanoid robot selection controls.

[0029] The busy / idle query reply message is used to instruct the user device to display the task configuration interface. The task selection control in the task configuration interface is used to display the corresponding task content. One task selection control corresponds to one task content, which is specifically a task content for the user to select for training a humanoid robot. The humanoid robot selection control in the task configuration interface is used to display the busy / idle status of the corresponding humanoid robot. One humanoid robot selection control corresponds to one humanoid robot.

[0030] In a specific implementation, after receiving the busy / idle query request, the server will query the busy / idle status of all humanoid robots to generate a query reply message. Alternatively, the server can query the busy / idle status of all robots before receiving the busy / idle query request, so that when receiving the busy / idle query request, the server can directly send a query reply message to the user device, thereby improving the response efficiency and enhancing the user experience on the user device side.

[0031] Specifically, the task content corresponding to any task selection control can be executed by any humanoid robot. At this time, in the task configuration interface, the task selection control and the humanoid robot selection control can be two parallel contents for the user to select. Alternatively, in the task configuration interface, the corresponding directory of any task selection control includes the humanoid robot selection controls corresponding to all humanoid robots. Alternatively, in the task configuration interface, the corresponding directory of the humanoid robot selection control corresponding to any humanoid robot includes all task selection controls.

[0032] Or, specifically, humanoid robots can be differentiated to perform different task contents, so as to further realize differentiated training of humanoid robots, so that the functions of trained humanoid robots are more targeted. At this time, in the task configuration interface, the directory corresponding to a single task selection control only includes the humanoid robot selection control corresponding to the humanoid robot that can be used to perform the task content corresponding to the task selection control. Or, in the task configuration interface, the directory corresponding to the humanoid robot selection control corresponding to a single humanoid robot includes the task selection control corresponding to the task content that the humanoid robot can perform.

[0033] Step S230: sending a task configuration message to the server in response to a second operation performed by the user on the user device, wherein the task configuration message carries a target humanoid robot and a target task.

[0034] The target humanoid robot is one of the humanoid robots corresponding to a plurality of humanoid robot selection controls, and the target task is one of the task contents corresponding to a plurality of task selection controls.

[0035] The second operation refers to a selection operation performed by the user on a control for initiating a task configuration message after the user performs a selection operation on a single task selection control and a single humanoid robot selection control in the task configuration interface displayed by the user device.

[0036] For example, see Figure 4 After the user device displays the task configuration interface 40, the user can perform a selection operation on one of all the displayed task selection controls 420, and perform a selection operation on one of all the displayed humanoid robot selection controls 430 that is in an idle state, and then perform a selection operation on the task confirmation control 410 to generate a task configuration message, and send the task configuration message to the server. Among them, the task content corresponding to the task selection control on which the user performs the selection operation is the target task, and the humanoid robot corresponding to the humanoid robot selection control on which the user performs the selection operation is the target humanoid robot.

[0037] Specifically, see Figure 4 , the task contents corresponding to the multiple task selection controls 420 include "desktop resource processing", "desktop garbage cleaning", "desktop item color classification", etc. When the user selects the task selection control 420 corresponding to "desktop item color classification", the humanoid robots that can be used to execute the task content include "robot A", "robot B" and "robot C", among which "robot A" and "robot B" are in an idle state, and their corresponding humanoid robot selection controls 430 can be selected as objects.

[0038] Step S240, receiving a training result message from the server, and displaying a training result interface, wherein the training result interface is used to display the training score of the target humanoid robot executing the target control solution.

[0039] The training result message is used to instruct the user device to display the training result interface.

[0040] Among them, the target control scheme is the content generated by the server based on the user demonstration data, the user demonstration data is the content uploaded to the server by the virtual reality device, and the user demonstration data is used to characterize the actions performed by the user for the target task.

[0041] In a specific implementation, after receiving the task configuration message from the user device, the server will obtain the user demonstration data uploaded by the virtual reality device, analyze and process the user demonstration data to generate a target control scheme, and then send a control instruction carrying the target control scheme to the target humanoid robot to instruct the target humanoid robot to train according to the target control scheme. The target humanoid robot will upload its training data for training, and the server can analyze and process the training data to obtain the training score of the target humanoid robot executing the target control scheme, thereby generating a training result message, and sending the training result message to the user device to instruct the user device to display the training result interface.

[0042] It can be seen that in this embodiment, the user device sends a busy / idle query request to the server in response to the first operation of the user acting on the user device; and receives a busy / idle query reply message from the server, and can display a task configuration interface, which includes multiple task selection controls and multiple humanoid robot selection controls, the task selection control is used to display the corresponding task content, and the humanoid robot selection control is used to display the busy / idle status of the corresponding humanoid robot. It is also possible to send a task configuration message to the server in response to the second operation of the user acting on the user device, the task configuration message carries a target humanoid robot and a target task, the target humanoid robot is one of the humanoid robots corresponding to the multiple humanoid robot selection controls, and the target task is one of the task contents corresponding to the multiple task selection controls; receive a training result message from the server, and display a training result interface, which is used to display the training score of the target humanoid robot executing the target control scheme, the target control scheme is the content generated by the server according to the user demonstration data, the user demonstration data is the content uploaded to the server by the virtual reality device, and the user demonstration data is used to characterize the actions performed by the user for the target task. It can be seen that in this application, the user can select the required humanoid robot selection control and task selection control in the task configuration interface displayed by the user device to determine the target humanoid robot to be trained and the target task for its corresponding training, and after the training is completed, the training result interface is displayed through the user device for the user to understand the training situation. In this way, the application function of remote training of humanoid robots can be realized, which is conducive to differentiated training of humanoid robots and improves the diversity and flexibility of humanoid robot training and learning.

[0043] In a possible example, the task selection control includes a first task control and a second task control, the task content corresponding to the first task control is a task performed on the upper body of a humanoid robot, and the task content corresponding to the second task control is a task performed on the whole body of the humanoid robot; when the target task is the task content corresponding to the first task control, the user demonstration data includes user posture data, and the user posture data is operation data collected by the virtual reality device; when the target task is the task content corresponding to the second task control, the user demonstration data includes the user posture data and task video data, and the task video data is video content of the user's whole body activities collected by the virtual reality device.

[0044] Specifically, during pre-configuration, the embodied task configuration system may further divide the task content into a first task type and a second task type, etc. For example, see Figure 4, task content refers to the content of the teleoperation task, which may include a first task type and a second task type. Among them, the task content corresponding to the first task control belongs to the first task type. The task content corresponding to the second task control belongs to the second task type. In this way, the user can select the first task control under the first task type or the second task control under the second task type according to the needs, so as to realize differentiated training of different humanoid robots, so as to make the functions of the humanoid robots more targeted. In a specific implementation, the components and structures of the humanoid robot used to perform the first task type and the humanoid robot used to perform the second task type can be configured differently, so as to reduce the production and manufacturing costs while meeting the function realization, and meet the use and purchase needs of different users.

[0045] In a specific implementation, user posture data refers to data collected by wearable devices such as data gloves and head-mounted display devices for collecting interactive data. Task video data refers to video data collected by a full-body motion capture device (such as an RGB camera). When the target task is the task content corresponding to the first task control, the target task that the target humanoid robot needs to perform is a task for the upper body. At this time, the server can generate a target control scheme based on the user posture data, so the user demonstration data collected by the virtual reality device can only include user posture data. When the target task is the task content corresponding to the second task control, the target task that the target humanoid robot needs to perform is a task for the whole body. At this time, the virtual reality device can further collect task video data on the basis of collecting user posture data to improve the accuracy and reliability of the results of the server's processing of user demonstration data. Exemplarily, tasks for the upper body can include desktop resource processing, desktop garbage cleaning, desktop item color classification, etc., and tasks for the whole body can include squatting to tie up the garbage bag in the trash can, taking it out, and placing it at the door, etc.

[0046] In a specific implementation, after the server receives the task configuration message from the user device, the server may also send a wearing prompt message to the user device, and the user device may display a wearing prompt interface according to the instruction of the wearing prompt message to prompt the user to wear the virtual reality device. After the user device displays the wearing prompt interface for prompting, the virtual reality device may detect whether the user has completed the wearing, and after the wearing is completed, prompt the user to perform corresponding actions according to the target task, so that the virtual reality device can collect the user demonstration data.

[0047] For example, see Figure 5 When the target task is the task content corresponding to the first task control, the wearing prompt interface can be displayed as follows: Figure 5As shown in (a) of FIG. 1 , the wearing prompt interface includes a first prompt area 510 for prompting the user to wear a head-mounted display device, and a second prompt area 520 for prompting the user to wear a wearable device such as a data glove for collecting interactive data. When the target task is the task content corresponding to the second task control, the display wearing prompt interface may also include the following: Figure 5 The content shown in (b) in the figure can be viewed by the user by sliding or turning pages. Figure 5 The contents shown in (a) and (b) in the above table are as follows. Figure 5 As shown in (b) in FIG. 5 , the wearing prompt interface may further include a third prompt area 530 for prompting the user to set up the motion capture device and be located within its collection range.

[0048] It can be seen that in this example, by classifying the task selection controls into first task controls and second task controls, and obtaining user posture data as user demonstration data corresponding to the first task control, and obtaining user posture data and task video data as user demonstration data corresponding to the second task control, the embodied task configuration system can achieve differentiated management of different types of tasks, so as to ensure the accuracy of the wear prompt content displayed on the user device side, which is conducive to improving the accuracy and reliability of data processing by the server.

[0049] In one possible example, the target control scheme is generated by the server according to the following steps: determining a task type of the target task, the task type including a first task type and a second task type, the first task type being a task type corresponding to a first task control, and the second task type being a task type corresponding to a second task control; if the task type of the target task is the first task type, determining an execution object according to the target task, and generating the target control scheme according to the execution object and the user posture data, the execution object including a left hand, a right hand, or both hands; if the task type of the target task is the second task type, analyzing the task video data to determine user motion posture data, and generating the target control scheme according to the user motion posture data and the user posture data.

[0050] Among them, the execution object is used to represent the limb structure used by the target humanoid robot when executing the training content corresponding to the target task.

[0051] In a specific implementation, after receiving the task configuration message from the user device, the server can obtain the user demonstration data uploaded by the virtual reality device, and analyze and process the user demonstration data to generate a target control solution. The specific analysis and processing process is as follows: the server first determines the task type corresponding to the currently executed target task, which can be data pre-stored by the server according to the task content, or it can be the content carried by the task configuration message.

[0052] When the server determines that the task type of the target task is the first task type, it determines the limb structure used by the user to perform the action of the target task based on the user posture data uploaded by the virtual reality device, and determines the execution object corresponding to the target humanoid robot based on the limb structure. If the user uses the left hand for the action, the execution object is the left hand of the target humanoid robot. Then the server determines the motion data of the execution object based on the joint movement direction and joint movement angle (data collected by the data glove) of the limb structure during the action, and generates a target control scheme for controlling the upper body of the target humanoid robot based on the motion data and the data captured by the head display device in the user posture data, so as to realize remote control training of the target humanoid robot. Among them, the data captured by the head display device is used to characterize the position and movement trajectory of the user's head, upper body and arms.

[0053] When the server determines that the task type of the target task is the second task type, it will obtain the task video data and user posture data uploaded by the virtual reality device. Based on the above data processing for the first task type, the task video data is subjected to posture analysis to obtain user motion posture data (such as upper body posture and lower body posture, arm and wrist posture, finger joint posture, etc.), thereby combining the user motion posture data and motion data to generate a target control scheme for controlling the entire body of the target humanoid robot, thereby realizing remote control training of the target humanoid robot. Alternatively, the server can combine the user motion posture data, motion data, and the data captured by the head display device in the user posture data to generate a target control scheme for controlling the entire body of the target humanoid robot, so as to improve the accuracy and reliability of the data.

[0054] It can be seen that in this example, the server adopts different data processing methods for target tasks of different task types, which can realize differentiated processing of data and improve the intelligence of data processing by the server.

[0055] In one possible example, before determining the task type of the target task, the step of generating the target control scheme also includes: determining a fluctuation value of the user posture data, wherein the fluctuation value is used to characterize the degree of stability of the user posture data; comparing the fluctuation value with a preset threshold; if the fluctuation value is greater than the preset threshold, smoothing the user posture data to obtain target posture data, wherein the target posture data is used to generate the target control scheme.

[0056] Among them, the preset threshold can be set according to needs.

[0057] In a specific implementation, when generating a target control scheme, the server can first determine the variance or standard deviation of the user's posture data, and determine the variance or standard deviation as the fluctuation value of the user's posture data, and then compare the fluctuation value with a preset threshold. If the fluctuation value is greater than the preset threshold, it indicates that the user's motion stability during the action is poor. At this time, the server can smooth the user's posture data through a smoothing algorithm such as a moving average or a Kalman filter to obtain the target posture data, thereby achieving noise reduction for the user's posture data and improving the accuracy and stability of the data. The target control scheme generated by the server based on the target posture data can ensure the stability of the target humanoid robot's motion during training. If the fluctuation value is less than or equal to the preset threshold, it indicates that the user's motion stability during the action meets the standard. At this time, the server can directly generate a target control scheme based on the user's posture data.

[0058] It can be seen that in this example, before generating the target control scheme, the server first determines the fluctuation value of the user posture data, and determines the stability of the user posture data by comparing the fluctuation value and the preset threshold, so as to smooth the user posture data when the stability of the user posture data is poor. This can improve the stability of the generated target control scheme in controlling the target humanoid robot's movements and optimize the training effect.

[0059] In a possible example, the training score is generated by the server according to the following steps: obtaining training data uploaded by the target humanoid robot, the training data including training time, preset training times and training success rate; and determining a training score based on the training data.

[0060] The training data is the data content generated during the training of the target humanoid robot according to the target control scheme. The training data includes training time, preset training times and training success rate. The training time is used to represent the total time for the target humanoid robot to complete the target task. The preset training times are used to represent the preset number of times the target humanoid robot performs the target task. The training success rate is used to represent the ratio of the total number of times the target humanoid robot completes the target task and the preset training times when performing the target task.

[0061] In a specific implementation, scoring rules may be pre-stored in the server, and the scoring rules are used to configure weights for training time, preset training times, and training success rate. Specifically, after the training of the target humanoid robot is completed, the training data will be uploaded to the server. After the server obtains the training data, it can convert the training time into a first value according to a first preset conversion rule, convert the preset training times into a second value according to a second preset conversion rule, and convert the training success rate into a third value according to a third preset conversion rule, and then determine the weighted sum of the first value, the second value, and the third value as the training score according to the scoring rules. For example, see Figure 6 The first display control 610 in the training result interface 60 is used to display the training score.

[0062] Specifically, the first preset rule is: a plurality of continuous duration ranges are divided for the target task, each duration range corresponds to a value, the duration range corresponding to the training time is determined, and the value corresponding to the duration range is determined as the first value. The second preset conversion rule and the third preset conversion rule can also be set in the same way, which will not be further described here.

[0063] It can be seen that in this example, the server determines the training score by comprehensively considering the training time, preset training times and training success rate of the target humanoid robot, which can ensure the intelligence and reliability of the determined training score.

[0064] In one possible example, the training result interface is also used to display the scoring result of points, and the points are used to redeem items according to preset scoring rules; the scoring result of the points is determined by the server according to the following steps: obtaining a preset basic score for a single training; determining the training score according to the training score and preset scoring rules; and summing the basic score and the training score to obtain the scoring result.

[0065] Among them, points are a kind of reward representation in the application provided by the embodied task configuration system. They are used to redeem coupons or commodities (such as humanoid robots and their accessories) and other items in the application.

[0066] In a specific implementation, after the server determines the training score, it can further calculate the training score corresponding to the training score according to the preset scoring rule, and then sum the training score and the preset basic score to obtain the score result of the score. For example, the preset scoring rule can be: if the training score is less than 80 points, 0 points are given; if the training score is between 80 and 90 points, 1 point is given; if the training score is between 90 and 95 points, 2 points are given; if the training score is between 95 and 100 points, 3 points are given. For example, see Figure 6 The second display control 620 in the training result interface 60 is used to display the score result of the points corresponding to the current training, and the third display control 630 is used to display the total points of the user. The user can jump to the points details interface by performing a selection operation on the third display control 630, and the points details interface is used to display the historical points and the historical use of the points.

[0067] For specific implementation, see Figure 6 The training result interface 60 may also include a historical training control, so as to query the user's historical training records and scores by triggering the historical training control.

[0068] It can be seen that in this example, by configuring the points redemption function for the application provided by the embodied task configuration system, the intelligence of the application provided by the embodied task configuration system can be improved. Determining the score result based on the preset basic score and training score can improve the flexibility and intelligence of the server in data processing, which is conducive to stimulating the user's enthusiasm for use and improving the user's seriousness in training humanoid robots.

[0069] In one possible example, after receiving the training result message from the server and displaying the training result interface, the method further includes: sending a machine query request to the server in response to a third operation performed by the user on the user device, the machine query request being used to query a recommended humanoid robot that is suitable for the user; receiving a query reply message from the server and displaying a product recommendation interface, the product recommendation interface being used to display device information of the recommended humanoid robot, the recommended humanoid robot being determined by the server according to the following steps: obtaining historical data of the user for the target task, the historical data including all training data of the user selecting different humanoid robots to perform the target task; determining, based on the historical data, the adaptation values ​​corresponding to all humanoid robots that perform the target task; comparing all the adaptation values, and determining that the humanoid robot corresponding to the largest adaptation value is the recommended humanoid robot.

[0070] The third operation refers to a selection operation performed by the user on the control in the interface displayed by the user device where the user initiates a machine query request. Figure 6 When the user performs a selection operation on the fourth display control 640 in the training result interface 60, the user device sends a machine query request to the server. The machine query request is used to query the recommended humanoid robot that is suitable for the user. The recommended humanoid robot refers to the humanoid robot that has the highest degree of adaptability to the user among all the humanoid robots used by the user for training.

[0071] In a specific implementation, after receiving a machine query request, the server will first determine the target task corresponding to the current training, and then capture the historical data corresponding to the target task. Thus, all humanoid robots used by the user for training for the target task and all training data corresponding to each humanoid robot are determined based on the historical data. Then, the adaptation value corresponding to each humanoid robot is confirmed based on the historical data. The adaptation value can be the average of the training scores corresponding to all training data corresponding to each humanoid robot. The server can then compare the adaptation values ​​corresponding to all humanoid robots, and determine the humanoid robot with the largest adaptation value as the recommended humanoid robot. After determining the recommended humanoid robot, the server can generate a query reply message and send the query reply message to the user device. The user device can display a product recommendation interface based on the query reply message to show the device information of the recommended humanoid robot to the user.

[0072] Specifically, the product recommendation interface may be a device introduction interface, which is only used to display the device information of the recommended humanoid robot. Alternatively, the product recommendation interface may be as follows: Figure 7 The product purchase interface shown in the product purchase interface displays the recommended humanoid robot, and the device information is the content displayed in the detailed information in the product purchase interface.

[0073] It can be seen that in this example, the user device sends a machine query request to the server in response to the third operation performed by the user on the user device, and receives a query reply message from the server to display a product recommendation interface, which is conducive to enriching the functionality of the application provided by the embodied task configuration system and providing users with a quick purchase path for recommended humanoid robots. At the same time, the server determines the humanoid robot with the largest adaptation value among multiple humanoid robots corresponding to the target task as the recommended humanoid robot, which is conducive to the intelligence and accuracy of the server's data processing and ensures the reliability of the adaptation between the user and the recommended humanoid robot.

[0074] The electronic device 80 in the present application may be structured as follows: Figure 8 As shown, the electronic device 80 is a computing device, which is a computer or a server, etc., and can be used to execute the above method. Specifically, the electronic device 80 may include a processor 810, a memory 820, a communication interface 830, and one or more programs 821, wherein the one or more programs 821 are stored in the above memory 820 and are configured to be executed by the above processor 810, and the one or more programs 821 include instructions for executing any step in the above method embodiment.

[0075] Among them, the communication interface 830 is used to support the communication between the electronic device 80 and other devices. The processor 810 can be, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, units and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0076] The memory 820 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory are available, such as static random access memory (Static RAM, SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch Link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM).

[0077] In a specific implementation, the processor 810 is used to execute any step in the above method embodiment, and when performing data transmission such as sending, the communication interface 830 can be selectively called to complete the corresponding operation.

[0078] It should be noted that the structural diagram of the above-mentioned electronic device 80 is only an example, and the specific components included may be more or less, and no sole limitation is made here.

[0079] The present application can divide the functional units of the electronic device according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0080] Fig. 9 1 is a functional unit block diagram of a first embodied task configuration device based on user demonstration data provided by an embodiment of the present application. The first embodied task configuration device based on user demonstration data 90 includes: A first responding unit 910 is configured to send a busy / idle query request to the server in response to a first operation performed by a user on the user equipment; A first receiving unit 920 is used to receive a busy / idle query reply message from the server, and display a task configuration interface, wherein the task configuration interface includes a plurality of task selection controls and a plurality of humanoid robot selection controls, wherein the task selection controls are used to display corresponding task contents, and the humanoid robot selection controls are used to display the busy / idle status of the corresponding humanoid robot; A second response unit 930 is configured to send a task configuration message to the server in response to a second operation performed by the user on the user device, wherein the task configuration message carries a target humanoid robot and a target task, wherein the target humanoid robot is one of the humanoid robots corresponding to a plurality of humanoid robot selection controls, and the target task is one of the task contents corresponding to the plurality of task selection controls; The second receiving unit 940 is used to receive a training result message from the server and display a training result interface, wherein the training result interface is used to display the training score of the target humanoid robot executing the target control scheme, wherein the target control scheme is content generated by the server based on user demonstration data, wherein the user demonstration data is content uploaded to the server by the virtual reality device, and wherein the user demonstration data is used to characterize the actions performed by the user for the target task.

[0081] In a possible example, the task selection control includes a first task control and a second task control, the task content corresponding to the first task control is a task performed on the upper body of a humanoid robot, and the task content corresponding to the second task control is a task performed on the whole body of the humanoid robot; when the target task is the task content corresponding to the first task control, the user demonstration data includes user posture data, and the user posture data is operation data collected by the virtual reality device; when the target task is the task content corresponding to the second task control, the user demonstration data includes the user posture data and task video data, and the task video data is video content of the user's whole body activities collected by the virtual reality device.

[0082] In one possible example, the target control scheme is generated by the server according to the following steps: determining a task type of the target task, the task type including a first task type and a second task type, the first task type being a task type corresponding to a first task control, and the second task type being a task type corresponding to a second task control; if the task type of the target task is the first task type, determining an execution object according to the target task, and generating the target control scheme according to the execution object and the user posture data, the execution object including a left hand, a right hand, or both hands; if the task type of the target task is the second task type, analyzing the task video data to determine user motion posture data, and generating the target control scheme according to the user motion posture data and the user posture data.

[0083] In one possible example, before determining the task type of the target task, the step of generating the target control scheme also includes: determining a fluctuation value of the user posture data, wherein the fluctuation value is used to characterize the degree of stability of the user posture data; comparing the fluctuation value with a preset threshold; if the fluctuation value is greater than the preset threshold, smoothing the user posture data to obtain target posture data, wherein the target posture data is used to generate the target control scheme.

[0084] In a possible example, the training score is generated by the server according to the following steps: obtaining training data uploaded by the target humanoid robot, the training data including training time, preset training times and training success rate; and determining a training score based on the training data.

[0085] In one possible example, the training result interface is also used to display the scoring result of points, and the points are used to redeem items according to preset scoring rules; the scoring result of the points is determined by the server according to the following steps: obtaining a preset basic score for a single training; determining the training score according to the training score and preset scoring rules; and summing the basic score and the training score to obtain the scoring result.

[0086] In one possible example, the first embodied task configuration device based on user demonstration data also includes a third response unit and a third receiving unit. After receiving the training result message from the server and displaying the training result interface, the third response unit is used to send a machine query request to the server in response to a third operation performed by the user on the user device, and the machine query request is used to query a recommended humanoid robot that is suitable for the user; the third receiving unit is used to receive a query reply message from the server and display a product recommendation interface, and the product recommendation interface is used to display the device information of the recommended humanoid robot, and the recommended humanoid robot is determined by the server according to the following steps: obtaining the user's historical data for the target task, and the historical data includes all training data of the user selecting different humanoid robots to perform the target task; determining the adaptation values ​​corresponding to all humanoid robots that perform the target task according to the historical data; comparing all the adaptation values, and determining that the humanoid robot corresponding to the largest adaptation value is the recommended humanoid robot.

[0087] In the case of using an integrated unit, the functional unit composition block diagram of the second embodied task configuration device based on user demonstration data provided in the embodiment of the present application is as follows: Fig.10 As shown. Fig.10 In the embodiment, the second embodied task configuration device 100 based on user demonstration data includes: a processing module 1020 and a communication module 1010. The processing module 1020 is used to control and manage the actions of the first embodied task configuration device 90 based on the user demonstration data, for example, the steps performed by the first response unit 910, the first receiving unit 920, the second response unit 930 and the second receiving unit 940, and / or other processes for performing the technology described herein. The communication module 1010 is used to support the interaction between the second embodied task configuration device 100 based on user demonstration data and other devices. Fig.10 As shown, the second embodied task configuration device 100 based on user demonstration data may further include a storage module 1030, and the storage module 1030 is used to store program codes and data of the second embodied task configuration device 100 based on user demonstration data.

[0088] Among them, the processing module 1020 can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor can also be a combination that implements a computing function, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 1010 can be a transceiver, an RF circuit or a communication interface, etc. The storage module 1030 can be a memory.

[0089] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here. The above-mentioned first embodied task configuration device based on user demonstration data or the second embodied task configuration device based on user demonstration data can both execute the above-mentioned Figure 2 The steps performed by the user device in the embodied task configuration method based on user demonstration data are shown.

[0090] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes a server.

[0091] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0094] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0097] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, ROM, RAM, a magnetic disk or an optical disk, etc.

[0098] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for configuring embodied tasks based on user demonstration data, characterized in that: A user device applied to an embodied task configuration system, the embodied task configuration system comprising a server, a humanoid robot, the user device and a virtual reality device, the server being communicatively connected to the humanoid robot, the user device and the virtual reality device respectively, the method comprising: Sending a busy / idle query request to the server in response to a first operation performed by a user on the user device; receiving a busy / idle query reply message from the server, and displaying a task configuration interface, wherein the task configuration interface includes a plurality of task selection controls and a plurality of humanoid robot selection controls, wherein the task selection controls are used to display corresponding task contents, and the humanoid robot selection controls are used to display the busy / idle status of the corresponding humanoid robot; In response to a second operation performed by the user on the user device, a task configuration message is sent to the server, wherein the task configuration message carries a target humanoid robot and a target task, wherein the target humanoid robot is one of the humanoid robots corresponding to a plurality of humanoid robot selection controls, and the target task is one of the task contents corresponding to the plurality of task selection controls; Receive a training result message from the server and display a training result interface, wherein the training result interface is used to display the training score of the target humanoid robot executing the target control scheme, wherein the target control scheme is content generated by the server based on user demonstration data, wherein the user demonstration data is content uploaded to the server by the virtual reality device, and wherein the user demonstration data is used to characterize the actions performed by the user for the target task.

2. The method according to claim 1, characterized in that The task selection control includes a first task control and a second task control, the task content corresponding to the first task control is a task performed on the upper body of the humanoid robot, and the task content corresponding to the second task control is a task performed on the whole body of the humanoid robot; When the target task is the task content corresponding to the first task control, the user demonstration data includes user posture data, and the user posture data is operation data collected by the virtual reality device; When the target task is the task content corresponding to the second task control, the user demonstration data includes the user posture data and task video data, and the task video data is the video content of the user's whole body activities collected by the virtual reality device.

3. The method according to claim 2, characterized in that The target control scheme is generated by the server according to the following steps: Determine a task type of the target task, where the task type includes a first task type and a second task type, where the first task type is a task type corresponding to a first task control, and the second task type is a task type corresponding to a second task control; If the task type of the target task is the first task type, determining an execution object according to the target task, and generating the target control scheme according to the execution object and the user posture data, the execution object including the left hand, the right hand or both hands; If the task type of the target task is the second task type, the task video data is analyzed to determine the user motion posture data, and the target control scheme is generated according to the user motion posture data and the user posture data.

4. The method according to claim 3, characterized in that Before determining the task type of the target task, the step of generating the target control scheme further includes: Determining a fluctuation value of the user posture data, wherein the fluctuation value is used to characterize a degree of stability of the user posture data; comparing the fluctuation value with a preset threshold; If the fluctuation value is greater than the preset threshold, the user posture data is smoothed to obtain target posture data, and the target posture data is used to generate the target control solution.

5. The method according to claim 1, characterized in that The training score is generated by the server according to the following steps: Acquire training data uploaded by the target humanoid robot, wherein the training data includes training time, preset training times, and training success rate; A training score is determined based on the training data.

6. The method according to claim 5, characterized in that The training result interface is also used to display the score result of the points, and the points are used to exchange items according to the preset score rules; the score result of the points is determined by the server according to the following steps: Get the preset basic score for a single training session; Determine a training score according to the training score and a preset scoring rule; The basic score and the training score are summed to obtain the score result.

7. The method according to claim 5, characterized in that After receiving the training result message from the server and displaying the training result interface, the method further includes: In response to a third operation performed by the user on the user device, a machine query request is sent to the server, where the machine query request is used to query a recommended humanoid robot suitable for the user; Receive a query reply message from a server and display a product recommendation interface, wherein the product recommendation interface is used to display the device information of the recommended humanoid robot, and the recommended humanoid robot is determined by the server according to the following steps: obtaining historical data of the user for the target task, wherein the historical data includes all training data of the user selecting different humanoid robots to perform the target task; determining the adaptation values ​​corresponding to all humanoid robots that perform the target task according to the historical data; comparing all the adaptation values, and determining that the humanoid robot corresponding to the largest adaptation value is the recommended humanoid robot.

8. An embodied task configuration device based on user demonstration data, characterized in that: A user device applied to an embodied task configuration system, the embodied task configuration system comprising a server, a humanoid robot, the user device and a virtual reality device, the server being communicatively connected to the humanoid robot, the user device and the virtual reality device respectively, the embodied task configuration device based on user demonstration data comprising: A first response unit, configured to send a busy / idle query request to the server in response to a first operation performed by a user on the user equipment; A first receiving unit is used to receive a busy / idle query reply message from the server and display a task configuration interface, wherein the task configuration interface includes a plurality of task selection controls and a plurality of humanoid robot selection controls, wherein the task selection controls are used to display corresponding task contents, and the humanoid robot selection controls are used to display the busy / idle status of the corresponding humanoid robot; a second response unit, configured to send a task configuration message to the server in response to a second operation performed by the user on the user device, wherein the task configuration message carries a target humanoid robot and a target task, wherein the target humanoid robot is one of the humanoid robots corresponding to a plurality of humanoid robot selection controls, and the target task is one of the task contents corresponding to the plurality of task selection controls; A second receiving unit is used to receive a training result message from the server and display a training result interface, wherein the training result interface is used to display the training score of the target humanoid robot executing the target control scheme, wherein the target control scheme is content generated by the server based on user demonstration data, wherein the user demonstration data is content uploaded to the server by the virtual reality device, and wherein the user demonstration data is used to characterize the actions performed by the user for the target task.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the steps in the method according to any one of claims 1 to 7.

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