Body-equipped robot control method, body-equipped robot and storage medium

Abstract

The invention discloses a control method of a robot with a body, the robot with the body and a storage medium, and the control method of the robot with the body comprises the steps: obtaining the pose change of a target object in a target home map; determining a target home task according to the pose change; and sending the target home task to one or more target body-equipped robots associated with the target home task, so that the target body-equipped robots execute the target home task. According to the method, the corresponding target body-equipped robot is autonomously controlled to execute the corresponding target home task based on the obtained pose change of the target object in the target home map, and the intelligent control degree of the body-equipped robot is improved.

Description

Technical Field

[0001] This application belongs to the technical field of smart home, and particularly relates to a control method for an embodied robot, an embodied robot, and a storage medium. Background Art

[0002] With the rapid development of technology, smart home systems have become popular in people's lives. Compared with traditional homes, more and more people choose to use smart home devices in smart home systems. For example, an embodied robot is used to perform home tasks.

[0003] Embodied robots can be divided into various types according to different functions and application scenarios, including unlocking robots, cleaning robots, curtain robots, and humanoid robots, etc.

[0004] Currently, during the process of an embodied robot performing home tasks, it usually executes corresponding home tasks according to the task program set by the user, and the smart home system cannot independently control the home robot to perform home tasks, resulting in a relatively low level of intelligent control of the embodied robot. Summary of the Invention

[0005] In view of this, embodiments of this application provide a control method for an embodied robot, an embodied robot, and a storage medium to overcome the above problems in the prior art.

[0006] In a first aspect, embodiments of this application provide a control method for an embodied robot, including: obtaining the pose change of a target object in a target home map; determining a target home task according to the pose change; and sending the target home task to one or more target embodied robots associated with the target home task, so that the target embodied robot executes the target home task.

[0007] In a second aspect, embodiments of this application provide a control device for an embodied robot. The control device for the embodied robot includes an obtaining module, a determining module, and a sending module. The obtaining module is configured to obtain the pose change of a target object in a target home map; the determining module is configured to determine a target home task according to the pose change; and the sending module is configured to send the target home task to one or more target embodied robots associated with the target home task, so that the target embodied robot executes the target home task.

[0008] In a third aspect, an embodiment of the present application provides an embodied robot, including a memory; one or more processors coupled to the memory; and one or more application programs. Among them, the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the control method of the embodied robot provided in the first aspect above.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which program code is stored, and the program code can be called by a processor to execute the control method of the embodied robot provided in the first aspect above.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a computer device, causes the computer device to execute the control method of the embodied robot provided in the first aspect above.

[0011] The solution provided by the present application obtains the pose change of the target object in the target home map, determines the target home task according to the pose change, and sends the target home task to one or more target embodied robots associated with the target home task, so that the target embodied robots execute the target home task, realizing autonomous control of the corresponding target embodied robots to execute the corresponding target home tasks based on the obtained pose change of the target object in the target home map, and improving the intelligent control degree of the embodied robots. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 FIG. shows a schematic diagram of a scenario of an embodied robot control system provided by an embodiment of the present application.

[0014] Figure 2 FIG. shows a schematic flowchart of a control method of an embodied robot provided by an embodiment of the present application.

[0015] Figure 3 FIG. shows another schematic flowchart of a control method of an embodied robot provided by an embodiment of the present application.

[0016] Figure 4Shows a schematic flowchart of obtaining a target three-dimensional home map in the control method of the embodied robot provided by an embodiment of the present application.

[0017] Figure 5 Shows a schematic flowchart of constructing a target three-dimensional home map based on an initial three-dimensional home map and second environmental data in the control method of the embodied robot provided by an embodiment of the present application.

[0018] Figure 6 Shows another schematic flowchart of the control method of the embodied robot provided by an embodiment of the present application.

[0019] Figure 7 Shows a schematic flowchart of a scenario of the control method of the embodied robot provided by an embodiment of the present application.

[0020] Figure 8 Shows a structural block diagram of a control device of the embodied robot provided by an embodiment of the present application.

[0021] Figure 9 Shows a functional block diagram of an embodied robot provided by an embodiment of the present application.

[0022] Figure 10 Shows a computer-readable storage medium for storing or carrying program code for implementing the control method of the embodied robot provided by an embodiment of the present application.

[0023] Figure 11 Shows a computer program product for storing or carrying program code for implementing the control method of the embodied robot provided by an embodiment of the present application. Detailed implementation manners

[0024] To make the objectives, features, and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0025] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0026] It should also be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0027] It should be further understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] In addition, in the description of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0029] With the rapid development of technology, smart home systems have become popular in people's lives. Compared with traditional homes, more and more people choose to use smart home devices in smart home systems. For example, they use embodied robots to perform home tasks.

[0030] According to different functions and application scenarios, embodied robots can be divided into multiple types, including unlocking robots, cleaning robots, curtain robots, and humanoid robots, etc.

[0031] Currently, during the process of an embodied robot performing home tasks, it usually executes corresponding home tasks according to the task program set by the user. The smart home system cannot independently control the home robot to perform home tasks, resulting in a relatively low level of intelligent control of the embodied robot.

[0032] In view of the above problems, the control method, embodied robot, and storage medium provided in the embodiments of this application, by obtaining the pose change of a target object in a target home map, determining a target home task according to the pose change, and sending the target home task to one or more target embodied robots associated with the target home task, enabling the target embodied robots to perform the target home task, realizes autonomous control of the corresponding target embodied robots to perform the corresponding target home tasks based on the obtained pose change of the target object in the target home map, and improves the intelligent control level of the embodied robot.

[0033] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application.

[0034] Please refer to Figure 1, which shows a schematic diagram of an application scenario of the embodied robot control system provided by an embodiment of the present application. The embodied robot control system may include an embodied robot 100, a camera 200, an object 300, a control device 400, etc. The embodied robot 100, the camera 200, the object 300, and the control device 400 are all installed in a home environment.

[0035] Among them, the embodied robot 100 is a movable home robot or a fixed home robot. The movable home robot can move in the home environment. For example, the movable home robot may include at least any one of a cleaning robot (such as a sweeping robot, a mopping robot, and a sweeping and mopping integrated robot), a robotic arm, a companion robot, and a humanoid robot, etc., which is not limited here.

[0036] The fixed home robot is fixedly installed in the home environment. For example, the fixed home robot may include at least any one of a curtain robot, a switch robot, and a lock-opening robot, etc., which is not limited here.

[0037] The camera 200 can be used to collect environmental data of the home environment. The camera 200 may include any one of a wide-angle camera, a macro camera, an ultra-wide-angle camera, a panoramic camera, a depth camera, a monocular camera, or a binocular camera, etc. The type of the camera 200 is not limited here and can be specifically set according to actual needs.

[0038] The object 300 may include at least any one of a user, a water cup, a sofa, a refrigerator, an air conditioner, a table and chair, a TV, and a trash can, etc., which is not limited here.

[0039] The control device 400 can be connected to the embodied robot 100 and the camera 200 through a network and perform data interaction with the embodied robot 100 and the camera 200 through the network.

[0040] The control device 400 may include any one of a terminal device and a server, etc. The type of the control device 400 is not limited here and can be specifically set according to actual needs.

[0041] The terminal device may be a mobile terminal device (for example, any one of a mobile phone, a personal digital assistant (PDA), a tablet personal computer (Tablet PC), a laptop computer, a smart watch, a smart bracelet, or a wearable device, etc.), or a fixed terminal device (such as a smart gateway device, a desktop computer, a smart panel, etc.), etc., which is not limited here.

[0042] The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), big data or artificial intelligence platforms, etc. Any of these is acceptable here and is not limited.

[0043] The network can be any one of a ZigBee network, a Bluetooth (BT) network, a Wireless Fidelity (Wi-Fi) network, a Thread network, a Long Range Radio (LoRa) network, a Low-Power Wide-Area Network (LPWAN), an infrared network, a Narrow Band Internet of Things (NB-IoT), a Controller Area Network (CAN), a Digital Living Network Alliance (DLNA) network, a Wide Area Network (WAN), a Local Area Network (LAN), a Metropolitan Area Network (MAN), or a Wireless Personal Area Network (WPAN), etc. Any of these is acceptable here and is not limited.

[0044] In some embodiments, the camera 200 may include multiple sub-cameras. The multiple sub-cameras can be installed at different positions in the home environment, and each sub-camera can be used to collect environmental data for different areas in the home environment.

[0045] The control device 400 can be connected to multiple sub-cameras through the network and perform data interaction with the multiple sub-cameras through the network.

[0046] Please refer to Figure 2 , which shows a flowchart of the control method for an embodied robot provided by an embodiment of the present application. In a specific embodiment, the control method for the embodied robot can be applied to the control device 400 in the embodied robot control system as shown in Figure 1 . Taking the application to the control device 400 as an example, the following will Figure 2The process shown is elaborated in detail. The control method of the embodied robot may include the following steps 110 to 130.

[0047] Step 110: Obtain the pose change of the target object in the target home map.

[0048] The target home map may be a target four-dimensional home map, and the target four-dimensional home map can be used to represent the pose change of the target object from the historical moment to the current moment.

[0049] The control device can determine the pose change of the target object in the target four-dimensional home map according to the first pose of the target object at the current moment and the second pose at the historical moment in the target four-dimensional home map, so as to realize real-time monitoring and preservation of the state of the target object in the home environment according to the target home map.

[0050] Among them, the first pose may include a first position and a first orientation, and the first orientation can be used to represent the direction of the target object in the target four-dimensional home map at the current moment.

[0051] The second pose may be the initial pose of the target object in the target four-dimensional home map, or the habitual pose of the target object in the target four-dimensional home map. Among them, the initial pose is the first pose saved in the target four-dimensional home map in chronological order, and the habitual pose can be obtained by learning multiple historical poses at multiple historical moments. The second pose is associated with the user's usage preference. When controlling the embodied robot to perform home tasks based on the user's usage preference, the embodied robot can be more in line with the user's needs, which is beneficial to improving the user experience in the process of controlling the embodied robot.

[0052] The second pose may include a second position and a second orientation, and the second orientation can be used to represent the direction of the target object in the target four-dimensional home map at the historical moment.

[0053] Regarding the process of the above control device obtaining the habitual pose by learning multiple historical poses at multiple historical moments, in some embodiments, the control device can obtain one historical pose corresponding to each historical moment, obtain multiple historical poses at multiple historical moments, and divide the same historical poses among the multiple historical poses into a group to obtain at least one historical pose group, and calculate the pose proportion of each historical pose group to obtain at least one pose proportion, and arrange the at least one pose proportion in descending order, and select the historical pose in one historical pose group corresponding to the pose proportion ranked first as the habitual pose.

[0054] Among them, all the historical poses in each historical pose group are the same. The control device can calculate the ratio of the number of the same historical poses in each historical pose group to the total number of multiple historical poses to obtain a pose proportion of the historical pose group. For example, if the target object is a water cup, the historical pose at 10:01:01 is Pose 1, the historical pose at 10:01:02 is Pose 1, the historical pose at 10:01:03 is Pose 2, and the historical pose at 10:01:04 is Pose 3. Two Poses 1 form the first historical pose group, one Pose 2 forms the second historical pose group, and one Pose 3 forms the third historical pose group. The pose proportion of Pose 1 is one half, and the pose proportions of Pose 2 and Pose 3 are both one quarter. Therefore, Pose 1 is taken as the habitual pose.

[0055] Step 120: Determine the target home task according to the pose change.

[0056] In the embodiment of the present application, the control device can search a preset home task table according to the pose change to obtain the target home task, and determine the target home task based on the corresponding relationship between the pose change of the preset target object and the home task, which improves the determination accuracy of the target home task.

[0057] Among them, the preset home task table can be used to represent the corresponding relationship between the pose change of the target object and the home task. The home task can include at least any one of an unlocking task, a cleaning task, a curtain closing task, etc., which is not limited here.

[0058] For example, the objects can include Object 1, Object 2, Object 3, Object 4, and Object 5. The pose changes can include Pose Change 1, Pose Change 2, and Pose Change 3. The home tasks can include Task 1, Task 2, Task 3, Task 4, Task 5, Task 6, Task 7, Task 8, Task 9, Task 10, Task 11, Task 12, Task 13, Task 14, and Task 15.

[0059] The corresponding relationship between the target object and its pose change and the home task can be shown in Table 1, that is, the preset home task table. The target home task can be obtained according to this corresponding relationship.

[0060] Table 1

[0061]

[0062] It should be noted that the corresponding relationship between the target object and its pose change and the home task is not limited to that shown in Table 1, and can be specifically set according to actual needs.

[0063] The target home task can be used to instruct the target embodied robot associated with the target home task to restore the first pose of the target object at the current moment in the target four-dimensional home map to the second pose at the historical moment. For example, if the target object is a trash can and the second pose is the habitual pose, which is in the corner of the living room in the home environment corresponding to the target home map, and the first pose at the current moment is next to the coffee table in the home environment corresponding to the target home map, then the target home task is to move the trash can next to the coffee table to the corner of the living room. By restoring the first pose to the second pose at the historical moment, intelligent automatic organization of various objects in the home environment can be achieved.

[0064] In some embodiments, the control device can determine the target home task according to the pose distance between the first pose of the target object in the target home map at the current moment and the poses of other objects in the target home map, as well as the pose change. Among them, the other objects in the target home map are the objects closest to the target object in the target home map.

[0065] Specifically, in the case where the pose of the target object changes, according to this pose change, judge the pose distance between the first pose and the poses of other objects. If the pose distance is less than the third preset distance threshold, then determine the target home task. For example, if the target object is a user and the object type of the other object is a sofa, when the user moves from the door to the sofa and the distance between the user and the sofa in the target home map is less than the third preset distance threshold, then determine the target home task as "turn on the TV".

[0066] Among them, the third preset distance can be set according to manual experience.

[0067] Optionally, the corresponding relationship among the pose change of the target object, the pose distance, and the target home task can be pre-constructed.

[0068] For example, the pose distance between the target object and a certain other object can include pose distance 1, pose distance 2, pose distance 3, pose distance 4, and pose distance 5, the pose change can include pose change 1, pose change 2, and pose change 3, and the home tasks can include task 1, task 2, task 3, task 4, task 5, task 6, task 7, task 8, task 9, task 10, task 11, task 12, task 13, task 14, and task 15.

[0069] The corresponding relationship between the pose distance of the target object, its pose change, and the home task can be shown in Table 2, that is, the preset home task table. According to this corresponding relationship, the target home task can be obtained.

[0070] Table 2

[0071]

[0072] It should be noted that the correspondence between the pose distance of the target object and its pose change and the home tasks is not limited to that shown in Table 2, and can be specifically set according to actual needs.

[0073] Step 130: Send the target home task to one or more target embodied robots associated with the target home task, so that the target embodied robots execute the target home task.

[0074] In the embodiment of the present application, the control device can send the target home task to the target embodied robot associated with the target home task through the network. Associating the target home task with the corresponding target embodied robot in advance can prevent the mis-control of an embodied robot that does not have the ability to execute the target home task from executing the target home task, resulting in the failure of the target home task execution. This is beneficial to improving the control success rate of controlling the embodied robot. The target embodied robot receives and executes the target home task, realizing the autonomous control of the corresponding target embodied robot to execute the corresponding target home task based on the obtained pose change of the target object in the target home map, and improving the intelligent control degree of the embodied robot.

[0075] It should be noted that the number of target embodied robots in this step can be one or more, which is not limited here. For example, if the target home task is to clean the room, the associated target embodied robots can include a floor cleaning robot (for floor cleaning), a humanoid robot (for tidying up various objects), a window cleaning robot (for window cleaning), etc. By sending the target home task to multiple target embodied robots, multiple embodied robots can simultaneously execute different tasks associated with the target home task, so as to improve the working efficiency of the target embodied robot in executing the target home task.

[0076] In some embodiments, before step 130, the control device can also determine whether the current moment is within the time period when the target home task can be executed. If so, step 130 is executed; if not, step 130 is not executed.

[0077] Among them, the user can preset the time period during which each target home task can be executed.

[0078] In some embodiments, the target object may include the user, and the pose change can be used to represent that the user moves to the door in the home environment corresponding to the target home map. For example, the pose change can be the change in the pose when the user moves from an arbitrary position (such as on the sofa) in the home environment to the door.

[0079] If the distance between the pose of the user at the current moment in the target home map and the pose of the door in the target home map is less than the first preset distance threshold, it is determined that the target home task includes an unlocking task, and the target embodied robot may include an unlocking robot associated with the unlocking task.

[0080] Among them, the pose information of the door is pre-saved in the target home map, and the first preset distance threshold can be set according to manual experience. For example, it can be 30 cm.

[0081] The control device can send the unlocking task to the unlocking robot through the network. The unlocking robot receives and responds to the unlocking task, opens the door lock, and autonomously controls the unlocking robot to open the door lock for the user based on the change in the pose of the user before moving in front of the door in the home environment, improving the user experience in the intelligent control process of the embodied robot.

[0082] In some embodiments, the target object may include a water cup, the pose change may be used to represent that the water cup moves from the table to the ground in the home environment corresponding to the target home map, the target home task may include a cleaning task, and the target embodied robot may include a cleaning robot associated with the cleaning task.

[0083] The control device can send the cleaning task to the cleaning robot through the network. The cleaning robot receives and responds to the cleaning task, and plans a moving path according to the target home map and the pose of the water cup at the current moment in the target home map (i.e., the pose of the water cup on the ground in the target home map), moves to the area where the water cup belongs (i.e., the area near the water cup, such as the area within a radius of 50 cm centered on the pose of the water cup), and cleans the ground. Based on the change in the pose of the water cup falling from the table in the home environment to the ground, the cleaning robot is autonomously controlled to clean the ground where it has fallen, improving the user experience in the intelligent control process of the embodied robot.

[0084] In some embodiments, the target object may include a water cup, the pose change may be used to represent that the water cup moves from the table to the ground in the home environment corresponding to the target home map, the target home task may include a cleaning task, and the target embodied robot may include a cleaning robot and a humanoid robot associated with the cleaning task.

[0085] The control device can send cleaning tasks to the cleaning robot and the humanoid robot through the network. The cleaning robot receives and responds to the cleaning tasks to clean the ground. The humanoid robot receives and responds to the cleaning tasks, and plans a movement path according to the target home map and the pose of the water cup in the target home map at the current moment, and moves to the area where the water cup belongs. Further, according to the poses of the water cup on the table and on the ground in the target home map, it plans the movement path for the water cup to be moved, moves the water cup from the ground to the table, and based on the change in the pose of the water cup falling from the table in the home environment to the ground, autonomously controls the cleaning robot to clean the dropped ground and controls the humanoid robot to move the water cup, improving the user experience in the intelligent control process of the embodied robot.

[0086] In some embodiments, the target object may include a user, and the pose change may be used to represent that the user moves to the bed in the home environment corresponding to the target home map. For example, the pose change may be the change in the pose when the user moves from any position in the home environment to the bed.

[0087] If the distance between the pose of the user in the target home map at the current moment and the pose of the bed in the target home map is less than the second preset distance threshold, it is determined that the target home task includes a curtain closing task, and the target embodied robot may include a curtain robot associated with the curtain closing task.

[0088] Among them, the pose information of the bed is stored in the target home map, and the second preset distance threshold can be set according to manual experience, for example, it can be 5 cm.

[0089] The control device can send the curtain closing task to the curtain robot through the network. The curtain robot receives and responds to the curtain closing task to close the curtain. Based on the change in the pose of the user moving to the bed in the home environment, the curtain robot is autonomously controlled to close the curtain, improving the user experience in the intelligent control process of the embodied robot.

[0090] The solution provided in this application, by obtaining the pose change of the target object in the target home map, determining the target home task according to the pose change, and sending the target home task to the target embodied robot associated with the target home task, enables the target embodied robot to execute the target home task, realizes autonomously controlling the corresponding target embodied robot to execute the corresponding target home task based on the obtained pose change of the target object in the target home map, and improves the intelligent control degree of the embodied robot.

[0091] Please refer to Figure 3 , which shows the flowchart of the control method of the embodied robot provided by another embodiment of this application. In a specific embodiment, the control method of the embodied robot can be applied to, for example, Figure 1The control device 400 in the embodied robot control system shown below will be described in detail by taking the application to the control device 400 as an example. Before step 110, the following steps 210 to 240 may be included. Figure 3 For the process shown, before step 110, the following steps 210 to 240 may be included.

[0092] Step 210: Obtain a target three-dimensional home map of the home environment.

[0093] In this embodiment, the control device may obtain a target three-dimensional home map of the home environment, and the target three-dimensional home map may be used to represent the three-dimensional space information of the home environment.

[0094] In some embodiments, as Figure 4 shown, step 210 may include the following steps 211 to 214.

[0095] Step 211: Obtain first environmental data collected when the embodied robot moves in the home environment.

[0096] Among them, the first environmental data may include a first environmental image, or may also include 3D point cloud data and a first environmental image.

[0097] In this embodiment, the embodied robot may include a mobile home robot. When the user needs to construct a map of the embodied robot control system, a mapping instruction may be sent to the control device. The control device receives and responds to the mapping instruction, sends a first acquisition instruction to the mobile home robot through the network. The mobile home robot receives and responds to the first acquisition instruction, moves and travels in the home environment, collects environmental data of the home environment to obtain the first environmental data, and sends the first environmental data to the control device through the network. The control device receives the first environmental data returned by the mobile home robot until the mobile home robot traverses all the rooms in the home environment.

[0098] Among them, the mobile home robot may be provided with a robot sensor and a robot camera, and the robot sensor and the robot camera move as the mobile home robot moves and travels in the home environment.

[0099] The mobile home robot may control the robot sensor to collect 3D point cloud data of objects that can be detected when the mobile home robot moves in the home environment in real time or at regular intervals, and control the robot camera to collect a first environmental image when the mobile home robot moves in the home environment in real time or at regular intervals.

[0100] The robot sensor may include any one of a lidar or a vision sensor, etc., and the robot camera may include any one of an RGB camera, a depth camera or a stereo camera, etc., which is not limited here.

[0101] In one embodiment, the mobile home robot can also control the robot camera to collect the first environmental image in real time or at regular intervals when the mobile home robot moves in the home environment, and obtain the 3D point cloud data of the detectable objects according to the first environmental image.

[0102] In some embodiments, the control device can detect the user's operation. When it is determined according to the detected user operation that the user has input a composition instruction for composing the embodied robot control system, that is, a composition instruction for composing the embodied robot control system is received. For example, when the user needs to construct a map of the embodied robot control system, the user can perform a touch operation on the operation panel of the control device. The control device responds to the user's touch operation, generates a corresponding touch signal, and analyzes the touch signal. When it is determined that the touch signal is a preset signal for characterizing the composition of the embodied robot control system, it is determined that a composition instruction for composing the embodied robot control system is received.

[0103] In some embodiments, the control device can be provided with a voice recognition module. When the user needs to construct a map of the embodied robot control system, the user can send a voice message within the voice collection range of the voice recognition module. The voice recognition module collects the voice message sent by the user, performs voice recognition on the collected voice message, and determines, according to the recognition result of the voice recognition, that the recognition result contains keywords for instructing the composition of the embodied robot control system, such as "construct a map of the embodied robot control system", or for example, "embodied robot control system" and "composition", etc., then it is determined that a composition instruction for composing the embodied robot control system is received.

[0104] As an example, the voice message sent by the user is: compose the embodied robot control system. Then the recognition result of the voice recognition contains the keywords "embodied robot control system" and "composition", and it is determined that a composition instruction for composing the embodied robot control system is received.

[0105] In some embodiments, the embodied robot control system can further include a client. The client is connected to the control device through a network and performs data interaction with the control device through the network.

[0106] When the user needs to construct a map of the embodied robot control system, a composition instruction can be sent to the client. The client receives and responds to the composition instruction, forwards the composition instruction to the control device through the network, and the control device receives the composition instruction forwarded by the client.

[0107] Among them, the client can be any one of a mobile client (such as a mobile phone client, a PDA client, a Tablet PC client, a laptop client, a smart watch client, a smart bracelet client, or a wearable client, etc.) or a fixed client (such as a desktop computer client, a smart panel client, etc.). The type of the client is not limited here and can be specifically set according to actual needs.

[0108] Step 212: Obtain an initial three-dimensional home map based on the first environmental data.

[0109] In this embodiment, optionally, the control device may fuse the 3D point cloud data and the first environmental image to obtain an initial three-dimensional home map with image features of the home environment.

[0110] Optionally, 3D point cloud data of the objects that can be detected is obtained according to the first environmental image, so as to construct an initial three-dimensional home map with image features of the home environment.

[0111] Optionally, the control device may depict an initial three-dimensional space image of the home environment according to the 3D point cloud data, determine the matching feature points between the initial three-dimensional space image and the first environmental image, and fuse the initial three-dimensional space image and the first environmental image according to the matching feature points to obtain an initial three-dimensional home map.

[0112] Step 213: Obtain second environmental data collected by a camera in the home environment.

[0113] In this embodiment, the control device may send a second acquisition instruction to the camera through the network. The camera receives and responds to the second acquisition instruction, collects environmental data of the home environment to obtain second environmental data, and sends the second environmental data to the control device through the network. The control device receives the second environmental data returned by the camera.

[0114] Among them, the second environmental data may at least include a second environmental image, and the second environmental image contains objects that the mobile home robot cannot detect. For example, as Figure 1 shown, due to height limitations, the mobile home robot (a sweeping robot) can detect the table, but cannot detect the water cup on the table. The camera installed on the ceiling in the home environment can capture a second environmental image containing the water cup.

[0115] In some embodiments, the second environmental image may include a plurality of third environmental images. The camera may include a plurality of sub-cameras, which may be installed at different positions in the home environment (such as at the corners of the ceiling, near the windows, above the doors, etc.). Additionally, the number of cameras is not limited. If conditions permit, it is optimal that the field of view of these cameras can cover the entire environmental space. Each sub-camera can be used to collect environmental data for different areas in the home environment to obtain a plurality of third environmental images.

[0116] The control device can send a second acquisition instruction to the plurality of sub-cameras through the network. Each sub-camera receives and responds to the second acquisition instruction, collects environmental data for the corresponding area covered by each sub-camera in the home environment, obtains a third environmental image for each sub-camera, and sends the third environmental image to the control device through the network. The control device receives a third environmental image returned by each sub-camera to obtain a plurality of third environmental images.

[0117] Step 214: Determine the target three-dimensional home map based on the initial three-dimensional home map and the second environmental data.

[0118] In this embodiment, the second environmental data includes the second environmental image. The pose of the camera in the initial three-dimensional home map can be confirmed according to the second environmental image. Further, the pose information of the missing objects included in the home environment in the initial three-dimensional home map can be determined through the second environmental image. Among them, the missing objects are in the second environmental image, and the pose information of the missing objects is saved in the initial three-dimensional home map to obtain the complete target three-dimensional home map corresponding to the home environment.

[0119] In some embodiments, the first environmental data includes the first environmental image, and the second environmental data includes the second environmental image. As Figure 5 shown, step 214 may include the following steps 215 to 216.

[0120] Step 215: Match the second environmental image and the first environmental image to obtain the third pose of the camera in the initial three-dimensional home map.

[0121] In this embodiment, the control device can match the image features in the second environmental image and the image features in the first environmental image to obtain the third pose of the camera in the initial three-dimensional home map.

[0122] Among them, the third pose may include a third position and a third orientation, and the third orientation can be used to characterize the direction of the camera in the initial three-dimensional home map.

[0123] In some embodiments, the camera may include multiple sub-cameras, and the multiple sub-cameras may be installed at different positions in the home environment. The second environmental image may include multiple third environmental images, and each third environmental image may correspond to a sub-camera.

[0124] The control device may match each third environmental image with the first environmental image to determine a third pose of each sub-camera in the initial three-dimensional map, so as to obtain multiple third poses.

[0125] Step 216: Determine the target three-dimensional home map according to the third pose, the second environmental image, and the initial three-dimensional home map.

[0126] In this embodiment, the control device may determine the pose offset of each object in the second environmental image relative to the camera, and determine the target three-dimensional home map according to the pose offset corresponding to each object, the third pose, and the initial three-dimensional home map; specifically, it may determine the pose offset of the objects that the mobile home robot cannot detect in the second environmental image relative to the camera.

[0127] Constructing the target three-dimensional home map according to the pose offset of the object relative to the camera and the third pose of the camera in the initial three-dimensional home map can supplement the pose information of the objects not available in the initial three-dimensional home map, and improve the integrity of the target three-dimensional home map.

[0128] Among them, the pose offset corresponding to each object can be used to represent the pose (i.e., position and direction) of each object relative to the camera in the second environmental image.

[0129] Regarding the process of the above control device determining the pose offset of each object in the second environmental image relative to the camera, in some embodiments, the control device may obtain the depth point cloud data of each object in the second environmental image, and determine the pose offset according to the depth point cloud data. Calculating the pose offset of each object according to the depth point cloud data improves the calculation accuracy of the pose offset.

[0130] Among them, for each object in the second environmental image, the control device may perform object recognition on the second environmental image, and estimate the distance between the object and the camera according to the depth estimation algorithm to obtain the depth point cloud data. Optionally, the target detection algorithm (such as YOLO, SSD, Faster R-CNN, etc.) may be first used to identify and generate the bounding box of the object in the second environmental image, then obtain the 3D point cloud data of the scene corresponding to the camera, further project the 3D point cloud data onto the second environmental image, and according to the bounding box of the object, screen out the pixel points located within the bounding box of the object in the projected second environmental image, and then extract the 3D point cloud data corresponding to these pixel points, that is, obtain the 3D point cloud data of the object.

[0131] For each object in the second environmental image, the control device can construct an object coordinate system corresponding to each object based on the geometric features of each object, determine the origin of the object coordinate system, and align the object coordinate system with the camera coordinate system of the camera according to the depth point cloud data, obtain the rotation matrix from the object coordinate system to the camera coordinate system, obtain the translation vector according to the position from the origin of the object coordinate system to the camera coordinate system, and determine the pose offset according to the rotation matrix and the translation vector. Align and translate the object coordinate system based on the depth point cloud data to obtain the pose offset, improving the accuracy of the pose offset.

[0132] The geometric features may include shape information for characterizing the three-dimensional features of the object, and position and contour information for characterizing the two-dimensional features of the object, etc.

[0133] In some embodiments, for each object in the second environmental image, determine the fourth pose of the object in the initial three-dimensional home map according to the pose offset associated with the object and the third pose, and save the fourth poses of multiple objects in the initial three-dimensional home map to obtain the target three-dimensional home map.

[0134] In some embodiments, the second environmental data may further include the object types of each object. The control device can determine the fourth pose of each object in the initial three-dimensional home map according to the pose offset and the third pose, and perform semantic annotation on each object in the initial three-dimensional home map according to the fourth pose and the object type to obtain the target three-dimensional home map. Semantically annotating the objects in the three-dimensional home map based on the pose and type of the objects to obtain the three-dimensional home map enriches the semantic information of the three-dimensional home map, which is beneficial to improving the control accuracy of controlling the embodied robot according to the three-dimensional home map.

[0135] Among them, the control device can calculate the sum of the pose offset and the third pose to obtain the fourth pose; the control device can extract image features from the second environmental image, input the extracted features into a classification model (for example, ResNet, Inception, etc.) to identify the type of the object, and then use a large language model (such as GPT-3) to generate a semantic interpretation of the recognition result, let the large language model generate natural language, so as to perform semantic annotation on the object to obtain the target three-dimensional home map with semantic annotation information for each object.

[0136] The fourth pose may include a fourth position and a fourth orientation, and the fourth orientation may be used to characterize the direction of the object in the target three-dimensional home map.

[0137] The object type may include at least any one of a first type for characterizing a user, a second type for characterizing a water cup, a third type for characterizing a sofa, a fourth type for characterizing a refrigerator, a fifth type for characterizing an air conditioner, a sixth type for characterizing a table and chair, a seventh type for characterizing a television, an eighth type for characterizing a trash can, etc., which is not limited here.

[0138] In some embodiments, the camera may include multiple sub-cameras. The multiple sub-cameras may be installed at different positions in the home environment. The second environmental image may include multiple third environmental images, and each third environmental image may correspond to a sub-camera.

[0139] The control device may determine the target three-dimensional home map based on multiple third poses, multiple third environmental images, and the initial three-dimensional home map.

[0140] Among them, the target three-dimensional home map can be constructed according to the pose offset of the same object relative to multiple sub-cameras and the multiple third poses of the multiple sub-cameras in the initial three-dimensional home map, improving the accuracy of the target three-dimensional home map. Since the multiple sub-cameras are installed at different positions in the home environment, the areas corresponding to the third environmental images collected by each sub-camera are different. Therefore, the target three-dimensional home map can also be constructed according to the pose offsets of different objects in different third environmental images collected by the multiple sub-cameras relative to the corresponding sub-cameras, and the corresponding third poses of the corresponding sub-cameras in the initial three-dimensional home map, improving the integrity of the target three-dimensional home map.

[0141] Step 220: Obtain the first pose of the target object in the target three-dimensional home map at the current moment.

[0142] In this embodiment, the control device may send a third acquisition instruction to the camera and / or the mobile home robot through the network at the current moment. The camera and / or the mobile home robot receive and respond to the third acquisition instruction, perform image acquisition on the home environment to obtain a fourth environmental image, and send the fourth environmental image to the control device through the network. The control device receives the fourth environmental image returned by the camera, performs image recognition on the target object in the fourth environmental image, estimates the distance between the target object and the camera according to the depth estimation algorithm to obtain the current depth point cloud data, constructs a target object coordinate system corresponding to the target object based on the geometric features of the target object, determines the origin of the target object coordinate system, aligns the target object coordinate system and the camera coordinate system according to the current depth point cloud data to obtain the current rotation matrix from the target object coordinate system to the camera coordinate system, obtains the current offset according to the position from the origin of the target object coordinate system to the camera coordinate system, and obtains the first pose of the target object in the target three-dimensional home map according to the current rotation matrix, the current offset, and the third pose.

[0143] Among them, the current rotation matrix can be used to represent the rotation direction of the target object relative to the first orientation, and the current offset can be used to represent the distance of the target object relative to the camera.

[0144] Step 230: Determine whether the pose of the target object has changed according to the fifth pose of the target object in the target three-dimensional home map at the previous moment.

[0145] In this embodiment, the control device can obtain the fifth pose of the target object in the target three-dimensional home map at the previous moment, and determine whether the pose of the target object has changed according to the fifth pose and the first pose.

[0146] Among them, the fifth pose is calculated based on the fifth environmental image of the target object captured by the camera at the previous moment. The process of calculating the fifth pose according to the fifth environmental image is similar to the process of calculating the first pose according to the fourth environmental image, which will not be elaborated here.

[0147] It should be noted that since the camera collects environmental images regularly (for example, 1 frame of image is collected every 0.1 s), the fifth environmental image captured by the camera at the previous moment can be, for example, the environmental image collected in the previous 0.1 s.

[0148] The fifth pose may include a fifth position and a fifth orientation, and the fifth orientation can be used to represent the direction of the target object in the target three-dimensional home map at the previous moment.

[0149] The control device can calculate the position difference between the fifth position and the first position, and the orientation difference between the fifth orientation and the first orientation respectively, and determine whether the pose of the target object has changed according to the orientation difference. The orientation difference can be used to represent the angular difference in the direction of the target object in the target three-dimensional home map at different moments.

[0150] When the position difference is greater than the position difference sum and / or the orientation difference is greater than the orientation difference threshold, it is determined that the pose of the target object has changed; when the position difference is less than or equal to the position difference sum and the orientation difference is less than or equal to the orientation difference threshold, it is determined that the pose of the target object has not changed.

[0151] Step 240: When it is determined that the pose of the target object has changed, record the pose change of the target object from the fifth pose to the first pose in the target three-dimensional home map according to time dynamics, and obtain the target four-dimensional home map.

[0152] In this embodiment, when the control device determines that the posture of the target object has changed, it can dynamically record the posture change of the target object from the fifth posture to the first posture in the target three-dimensional home map according to time, and obtain the target four-dimensional home map; optionally, it can record the time corresponding to the previous moment and the current moment, as well as the fifth posture and the first posture, realizing the generation of the four-dimensional home map according to the posture change of the object in the three-dimensional home map at different moments, and can independently control the corresponding embodied robot to execute corresponding home tasks according to the change of the posture of the target object in the four-dimensional home map, which is beneficial to improving the intelligent control degree of the embodied robot.

[0153] Furthermore, a three-dimensional home map is constructed based on the data collected by the mobile home robot and the data collected by the camera in the home environment, which can suppress the problem that the data collected is missing information due to the height limitation of the mobile home robot, resulting in an incomplete three-dimensional home map constructed only based on the data collected by the mobile home robot and unable to reflect the environmental information of the entire home environment, and improves the construction integrity of the three-dimensional home map.

[0154] Please refer to Figure 6 , which shows a flowchart of a control method for an embodied robot provided in another embodiment of the present application. In a specific embodiment, the control method for the embodied robot can be applied to a control device 400 in an embodied robot control system as shown in Figure 1 . Taking the application to the control device 400 as an example, the process shown in Figure 6 will be elaborated in detail below. The control method for the embodied robot may include the following steps 310 to step 360.

[0155] Step 310: Obtain a target three-dimensional home map in the home environment.

[0156] Step 320: Obtain the first posture of the target object in the target three-dimensional home map at the current moment.

[0157] Step 330: Determine whether the posture of the target object has changed according to the fifth posture of the target object in the target three-dimensional home map at the previous moment.

[0158] Step 340: When it is determined that the posture of the target object has changed, record the posture change of the target object from the fifth posture to the first posture in the target three-dimensional home map dynamically according to time, and obtain the target four-dimensional home map.

[0159] In this embodiment, steps 310, 320, 330, and 340 can refer to the content of the corresponding steps in the foregoing embodiments, and will not be elaborated here.

[0160] Step 350: Generate the current movement trajectory of the target object according to the first posture and the fifth posture.

[0161] In this embodiment, when the control device determines that the posture of the target object has changed, it records the posture change of the target object from the fifth posture to the first posture in the target three-dimensional home map dynamically according to time. After obtaining the four-dimensional home map, the current movement trajectory of the target object can be generated according to the first posture and the fifth posture.

[0162] Among them, the current movement trajectory may include a current translation trajectory and / or a current rotation trajectory. The current translation trajectory can be used to represent the path of the target object translated from the fifth position to the first position, and the current rotation trajectory can be used to represent the angular change of the target object rotated from the fifth orientation to the first orientation.

[0163] Step 360: Generate a target movement trajectory according to the historical movement trajectory and the current movement trajectory of the target object.

[0164] In this embodiment, the control device can add the current movement trajectory of the target object to the historical movement trajectory to update the latest movement trajectory of the target object to obtain the target movement trajectory, and save the target movement trajectory in the target four-dimensional home map. The target movement trajectory of the target object can be displayed in the target four-dimensional home map. Based on the real-time update of the historical posture change by the current posture change of the target object, it is beneficial to improve the control accuracy of controlling the embodied robot, and the user can find the target object according to the target movement trajectory of the target object in the target home four-dimensional map. For example, the user can find the mobile phone according to the movement trajectory of the mobile phone, which improves the convenience.

[0165] Among them, the historical movement trajectory may include a historical translation trajectory and / or a historical rotation trajectory. The historical translation trajectory can be used to represent the corresponding historical movement path of the target object, and the historical rotation trajectory can be used to represent the corresponding historical angular change of the target object.

[0166] In an application scenario, as Figure 7 shown, the control method of the embodied robot may include the following steps 410 to 490.

[0167] Step 410: Obtain a target three-dimensional home map in the home environment.

[0168] Step 420: Obtain the first posture of the target object in the target three-dimensional home map at the current moment.

[0169] Step 430: Determine whether the posture of the target object has changed according to the fifth posture of the target object in the target three-dimensional home map at the previous moment.

[0170] Step 440: When it is determined that the pose of the target object has changed, record the pose change of the target object from the fifth pose to the first pose in the target three-dimensional home map dynamically according to time, and obtain the target four-dimensional home map.

[0171] Step 450: Generate the current movement trajectory of the target object according to the first pose and the fifth pose.

[0172] Step 460: Generate the target movement trajectory according to the historical movement trajectory and the current movement trajectory of the target object.

[0173] Step 470: Obtain the pose change of the target object in the target home map.

[0174] Step 480: Determine the target home task according to the pose change.

[0175] Step 490: Send the target home task to one or more target embodied robots associated with the target home task, so that the target embodied robots execute the target home task.

[0176] The solution provided in this embodiment, based on the real-time update of the historical pose change by the current pose change of the target object, is beneficial to improving the control accuracy of controlling the embodied robot, and the user can find the target object according to the target movement trajectory of the target object in the target four-dimensional home map. For example, the user can find the mobile phone according to the movement trajectory of the mobile phone, which improves the convenience.

[0177] Please refer to Figure 8 , which shows a control device 500 of an embodied robot provided in an embodiment of the present application. In a specific embodiment, the control device 500 of the embodied robot can be applied to a control device 400 in an embodied robot control system as shown in Figure 1 As shown, taking the application to the control device 400 as an example, the control device 500 of the embodied robot shown in Figure 8 will be elaborated in detail. The control device 500 of the embodied robot may include an acquisition module 510, a determination module 520, and a sending module 530.

[0178] The acquisition module 510 can be used to acquire the pose change of the target object in the target home map; the determination module 520 can be used to determine the target home task according to the pose change; the sending module 530 can be used to send the target home task to one or more target embodied robots associated with the target home task, so that the target embodied robots execute the target home task.

[0179] In some embodiments, the target home task can be used to instruct the target embodied robot to restore the first pose of the target object at the current moment in the target home map to the second pose at the historical moment.

[0180] In some embodiments, the second pose may be the initial pose of the target object in the target home map or the habitual pose of the target object in the target home map; the habitual pose may be obtained by learning multiple historical poses at multiple historical moments.

[0181] In some embodiments, the determination module 520 may include a first determination unit.

[0182] The first determination unit may be configured to determine a target home task based on the pose distance between the first pose of the target object in the target home map at the current moment and the poses of other objects in the target home map, as well as the pose change.

[0183] In some embodiments, the target object may include a user, the pose change may be used to represent that the user moves to the door in the home environment corresponding to the target home map, the target home task may include an unlocking task, and the target embodied robot may include an unlocking robot associated with the unlocking task; the determination module 520 may further include a second determination unit.

[0184] The second determination unit may be configured to determine that the target home task is an unlocking task if the distance between the pose of the user in the target home map at the current moment and the pose of the door in the target home map is less than a first preset distance threshold.

[0185] In some embodiments, the sending module 530 may include a first sending unit.

[0186] The first sending unit may be configured to send the unlocking task to the unlocking robot, so that the unlocking robot executes the unlocking task.

[0187] In some embodiments, the target object may include a water cup, the pose change may be used to represent that the water cup moves from the table to the ground in the home environment corresponding to the target home map, the target home task may include a cleaning task, and the target embodied robot may include a cleaning robot associated with the cleaning task; the sending module 530 may further include a second sending unit.

[0188] The second sending unit may be configured to send the cleaning task to the cleaning robot, so that the cleaning robot moves to the area where the water cup is located according to the target home map and the pose of the water cup in the target home map at the current moment, and executes the cleaning task to clean the ground.

[0189] In some embodiments, the target embodied robot may further include a humanoid robot associated with the cleaning task; the sending module 530 may further include a third sending unit.

[0190] The third sending unit can be used to send a cleaning task to the humanoid robot, so that the humanoid robot executes the cleaning task to move the water cup to the table according to the poses of the water cup on the table and on the ground in the target home map.

[0191] In some embodiments, the target object may include a user, the pose change may be used to represent that the user moves to the bed in the home environment corresponding to the target home map, the target home task may include a curtain closing task, and the target embodied robot may include a curtain robot associated with the curtain closing task; the determining module 520 may further include a third determining unit.

[0192] The third determining unit can be used to determine that the target home task is a curtain closing task if the distance between the pose of the user in the target home map at the current moment and the pose of the bed in the target home map is less than a second preset distance threshold.

[0193] In some embodiments, the sending module 530 may further include a fourth sending unit.

[0194] The fourth sending unit can be used to send a curtain closing task to the curtain robot, so that the curtain robot executes the curtain closing task.

[0195] It should be noted that the various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments. For any processing method described in the method embodiments, it can be implemented by the corresponding processing module in the device embodiments, and will not be elaborated one by one in the device embodiments.

[0196] In addition, in each embodiment of the present application, the various functional modules can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0197] Please refer to Figure 9 , which shows a functional block diagram of an embodied robot 600 provided by an embodiment of the present application. The embodied robot 600 may include one or more of the following components: a memory 610, a processor 620, and one or more application programs, where one or more application programs may be stored in the memory 610 and configured to be executed by one or more processors 620, and one or more application programs are configured to execute the methods described in the foregoing method embodiments.

[0198] The memory 610 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. The memory 610 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 610 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as obtaining pose changes, determining target home tasks, sending target home tasks, executing target home tasks, moving to the door, sending unlocking tasks, executing unlocking tasks, moving to the ground, sending cleaning tasks, executing cleaning tasks, moving to the bed, sending curtain closing tasks, executing curtain closing tasks, looking up a preset home task table, obtaining target home tasks, and restoring poses, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data created during the use of the embodied robot 600 (such as embodied robots, target objects, target home maps, pose changes, target home tasks, target embodied robots, users, home environments, doors, unlocking tasks, unlocking robots, water cups, desktops, floors, cleaning tasks, cleaning robots, humanoid robots, beds, curtain closing tasks, curtain robots, preset home task tables, current time, first pose, historical time, second pose, initial pose, habitual pose, multiple historical times, and multiple historical poses), etc.

[0199] The processor 620 may include one or more processing cores. The processor 620 connects various parts within the entire embodied robot 600 using various interfaces and lines. By running or executing instructions, programs, code sets or instruction sets stored in the memory 610, and by calling data stored in the memory 610, the processor 620 performs various functions of the embodied robot 600 and processes data. Optionally, the processor 620 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 620 may integrate a combination of one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interfaces, and application programs, etc.; the GPU is responsible for rendering and drawing display content; the modem is used to process wireless communications. It can be understood that the above modem may not be integrated into the processor 620 and may be implemented separately through a communication chip.

[0200] Please refer to Figure 10 , which shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application. Program code 710 is stored in the computer-readable storage medium 700, and the program code 710 can be called by a processor to execute the method described in the above method embodiment.

[0201] The computer-readable storage medium 700 can be an electronic memory such as a flash memory, EEPROM (electrically erasable programmable read-only memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 700 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 700 has a storage space for the program code 710 that executes any method step in the above method. These program codes can be read out from or written into one or more computer program products. The program code 710 can be compressed in an appropriate form, for example.

[0202] Please refer to Figure 11 , which shows a structural block diagram of a computer program product 800 provided by an embodiment of the present application. The computer program product 800 includes a computer program / instructions 810, and the computer program / instructions 810 are stored in a computer-readable storage medium of a computer device. When the computer program product 800 runs on the computer device, the processor of the computer device reads the computer program / instructions 810 from the computer-readable storage medium, and the processor executes the computer program / instructions 810, so that the computer device executes the method described in the above method embodiment.

[0203] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for an embodied robot, characterized in that: include: Obtain the position change of the target object in the target home map; determining a target home task according to the posture change; The target household task is sent to one or more target embodied robots associated with the target household task, so that the target embodied robots perform the target household task.

2. The control method according to claim 1, characterized in that: The target home task is used to instruct the target embodied robot to restore the first posture of the target object in the target home map at the current moment to the second posture at a historical moment.

3. The control method according to claim 2, characterized in that: The second posture is the initial posture of the target object in the target home map, or is the habitual posture of the target object in the target home map; the habitual posture is obtained based on learning multiple historical postures at multiple historical moments.

4. The control method according to claim 1, characterized in that: The determining of the target home task according to the posture change includes: The target home task is determined according to a posture distance between a first posture of the target object in the target home map at a current moment and postures of other objects in the target home map, as well as the posture change.

5. The control method according to claim 1, characterized in that: The target object includes a user, the posture change is used to represent that the user moves to the door in the home environment corresponding to the target home map, the target home task includes a lock-unlocking task, and the target embodied robot includes a lock-unlocking robot associated with the lock-unlocking task; The determining of the target home task according to the posture change includes: If the distance between the current position of the user in the target home map and the position of the door in the target home map is less than a first preset distance threshold, determining that the target home task is the unlocking task; The sending the target household task to one or more target embodied robots associated with the target household task, so that the target embodied robots perform the target household task, includes: The unlocking task is sent to the unlocking robot so that the unlocking robot performs the unlocking task.

6. The control method according to claim 1, characterized in that: The target object includes a water cup, the posture change is used to represent that the water cup moves from a tabletop to the ground in the home environment corresponding to the target home map, the target home task includes a cleaning task, and the target embodied robot includes a cleaning robot associated with the cleaning task; The sending the target household task to one or more target embodied robots associated with the target household task, so that the target embodied robots perform the target household task, includes: The cleaning task is sent to the cleaning robot, so that the cleaning robot moves to the area where the water cup belongs to perform the cleaning task according to the target home map and the current position of the water cup in the target home map to clean the floor.

7. The control method according to claim 6, characterized in that: The target embodied robot also includes a humanoid robot associated with the cleaning task; sending the target household task to one or more target embodied robots associated with the target household task so that the target embodied robots perform the target household task includes: The cleaning task is sent to the humanoid robot so that the humanoid robot performs the cleaning task to move the water cup to the tabletop according to the positions of the water cup on the tabletop and on the ground in the target home map.

8. The control method according to claim 1, characterized in that: The target object includes a user, the posture change is used to represent that the user moves to the bed in the home environment corresponding to the target home map, the target home task includes a curtain closing task, and the target embodied robot includes a curtain robot associated with the curtain closing task; The determining of the target home task according to the posture change includes: If the distance between the current position of the user in the target home map and the position of the bed in the target home map is less than a second preset distance threshold, determining that the target home task is the curtain closing task; The sending the target household task to one or more target embodied robots associated with the target household task, so that the target embodied robots perform the target household task, includes: The curtain closing task is sent to the curtain robot, so that the curtain robot performs the curtain closing task.

9. An embodied robot, characterized in that: include: Memory; One or more processors coupled to the memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the control method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the control method according to any one of claims 1 to 8.

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