Robot control method and apparatus, electronic device, storage medium, and product

Abstract

A robot control method, comprising: in response to an input of a user, acquiring dialogues of a plurality of robots regarding the input of the user, wherein the plurality of robots are adversarial; performing conflict detection on the dialogues of the plurality of robots so as to determine a conflicting dialogue and the type of conflict; sending an adjustment indication to at least one robot involved in the conflict, so that each adjusted robot continues a dialogue with other robots on the basis of the adjustment indication, the adjustment indication being generated on the basis of the input of the user and the type of conflict; and in response to the dialogues of the plurality of robots comprising a response to the input of the user, and no conflict being present in the dialogues of the plurality of robots, displaying the response. Also provided are a robot control apparatus, an electronic device, a storage medium, and a product.

Description

Robot control method, device, electronic device, storage medium and product Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a robot control method, device, electronic device, storage medium and product. Background Art

[0002] Robots powered by AI technology can provide feedback based on the information they receive. For example, in a conversational scenario, a robot can generate its own dialogue based on the conversations of other speakers. Robots often rely on machine learning models to generate responses. These models are trained using data from specific domains, enabling the robot to respond to specific domain characteristics. Machine learning models also refer to the robot's settings when processing conversations from other speakers to generate dialogue that meets these settings.

[0003] Summary of the Invention

[0004] This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] According to some embodiments of the present disclosure, a robot control method is provided, comprising: in response to user input, obtaining a conversation of multiple robots with respect to the user's input, wherein the multiple robots are antagonistic with each other; performing conflict detection on the conversations of the multiple robots to determine the conversations in which conflicts exist and the types of conflicts; sending an adjustment instruction to at least one robot involved in the conflict so that each adjusted robot continues to have a conversation with the other robots based on the adjustment instruction, wherein the adjustment instruction is generated based on the user's input and the type of conflict; and displaying a response in response to the conversation of the multiple robots including a response with respect to the user's input and the absence of conflicts in the conversations of the multiple robots.

[0006] According to some other embodiments of the present disclosure, a robot control device is provided, comprising: an acquisition module configured to acquire, in response to a user's input, a dialogue of multiple robots with respect to the user's input, wherein the multiple robots are antagonistic with each other; a detection module configured to perform conflict detection on the dialogues of the multiple robots to determine the dialogues in which conflicts exist and the types of conflicts; a sending module configured to send an adjustment instruction to at least one robot involved in the conflict, so that each adjusted robot continues to dialogue with the other robots based on the adjustment instruction, the adjustment instruction being generated based on the user's input and the type of conflicts; and a display module configured to display a response in response to the dialogue of the multiple robots including a response to the user's input and the absence of conflicts in the dialogues of the multiple robots.

[0007] According to some embodiments of the present disclosure, an electronic device is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the robot control method of any embodiment of the present disclosure based on instructions stored in the memory.

[0008] According to some embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the robot control method of any embodiment of the present disclosure is performed.

[0009] According to some embodiments of the present disclosure, a computer program product is provided. When the computer program product is run on a computer, the computer is enabled to implement the robot control method of any one of the embodiments of the present disclosure.

[0010] According to some embodiments of the present disclosure, there is provided a computer program comprising: instructions, which, when executed by a processor, cause the processor to execute the robot control method of any one of the embodiments of the present disclosure.

[0011] Other features, aspects and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The preferred embodiments of the present disclosure are described below with reference to the accompanying drawings. The drawings described herein are used to provide a further understanding of the present disclosure. Each of the drawings, together with the following detailed description, is included in this specification and forms a part of the specification to explain the present disclosure. It should be understood that the drawings described below only relate to some embodiments of the present disclosure and do not constitute a limitation of the present disclosure. In the drawings:

[0013] FIG1 shows a schematic flow chart of a method for controlling a robot according to some embodiments of the present disclosure.

[0014] FIG2 shows a schematic flow chart of a conflict detection method according to some embodiments of the present disclosure.

[0015] FIG3 shows a schematic flow chart of a conflict detection method according to other embodiments of the present disclosure.

[0016] FIG4 shows a schematic flow chart of a method for generating an adjustment indication according to some embodiments of the present disclosure.

[0017] FIG5A shows a schematic diagram of a user interface according to some embodiments of the present disclosure.

[0018] FIG5B shows a schematic diagram of a user interface according to some other embodiments of the present disclosure.

[0019] FIG5C shows a schematic diagram of a user interface according to yet other embodiments of the present disclosure.

[0020] FIG6 shows a schematic structural diagram of a control device of a robot according to some embodiments of the present disclosure.

[0021] FIG7 shows a schematic structural diagram of an electronic device according to some embodiments of the present disclosure.

[0022] FIG8 shows a schematic structural diagram of a computer system according to some embodiments of the present disclosure.

[0023] It should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not necessarily drawn to scale. The same or similar reference numerals are used throughout the drawings to indicate the same or similar parts. Therefore, once an item is defined in one drawing, it may not be discussed further in subsequent drawings. DETAILED DESCRIPTION

[0024] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. However, it is obvious that the embodiments described are only some embodiments of the present disclosure, rather than all embodiments. The following description of the embodiments is actually only illustrative and is in no way intended to limit the present disclosure and its application or use. It should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure can be performed in different orders and / or performed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect. Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments should be interpreted as being merely exemplary and do not limit the scope of the present disclosure.

[0026] As used in this disclosure, the term "include" and its variations are intended to be open-ended terms that include at least the following elements / features but do not exclude other elements / features, i.e., "including but not limited to." Furthermore, the term "comprise" and its variations are intended to be open-ended terms that include at least the following elements / features but do not exclude other elements / features, i.e., "including but not limited to." Therefore, "include" and "include" are synonymous. The term "based on" means "based, at least in part, on."

[0027] Reference throughout this specification to "one embodiment," "some embodiments," or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. For example, the term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Furthermore, the appearances of the phrases "in one embodiment," "in some embodiments," or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may.

[0028] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules, or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules, or units. Unless otherwise specified, concepts such as "first" and "second" are not intended to imply that the objects described in such a manner must be in a given order in time, space, ranking, or any other manner.

[0029] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0030] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0031] The following detailed description of the embodiments of the present disclosure is provided in conjunction with the accompanying drawings, but the present disclosure is not limited to these specific embodiments. The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. In addition, in one or more embodiments, specific features, structures, or characteristics may be combined in any suitable manner that will be apparent to those skilled in the art from this disclosure.

[0032] Since robots can generate dialogues based on input information, users can obtain information by communicating with robots. For example, robots can be used as search tools to provide users with search results, and as consulting tools to provide users with solutions to certain tasks. However, the content provided by a robot may have limitations, making the response provided to the user inaccurate and reducing the user's information acquisition efficiency. Therefore, the present disclosure utilizes multiple adversarial robots to jointly respond to the user's input, so that the response can take into account the dimensions referenced by multiple robots. The following describes an embodiment of the control method of the robot of the present disclosure with reference to Figure 1.

[0033] Figure 1 shows a schematic flow chart of a robot control method according to some embodiments of the present disclosure. As shown in Figure 1 , the control method of this embodiment includes steps S102 to S108.

[0034] In step S102 , in response to the user's input, a dialogue between multiple robots regarding the user's input is obtained, wherein the multiple robots are antagonistic with each other.

[0035] Users can input data through their device, for example, through an input interface provided by an application on the device. User input can be a task assigned by the user to the robot, which can be represented by at least one of text, audio, images, and video. User input involves multiple dimensions, including semantics and the processing flow used to process the input. For example, semantics can encompass multiple dimensions based on the literal meaning of the user's input, or it can be determined to encompass multiple dimensions through a correlation search and analysis of the topics covered by the user's input. For example, if the user input is "Please recommend me a mobile phone with good performance and low price," the literal semantics can reveal that it involves the dimensions of performance and price. For another example, if the user input is "Please recommend me a mobile phone," semantic analysis reveals that the topic is mobile phone recommendations. By searching and analyzing information related to mobile phone recommendations, it can be determined that the topic of mobile phone recommendations may involve multiple dimensions, such as performance, price, and appearance.

[0036] The robot in the embodiments of the present disclosure refers to an intelligent agent that can generate a response to input information, which can be implemented in the form of software, hardware, or a combination of software and hardware. The robot can also be called a digital human or a virtual agent of a machine learning model. The robot can be implemented based on a machine learning model, for example, based on a large language model (LLM) or a foundation model. The machine learning model can be a generative model, which is used to output target content based on the input information. The information input to the generative model includes the processing basis of the generative model during the generation process, such as which information is used to perform the generation process, the requirements for the output target content, etc. The generative model includes, for example, a model generated based on text, or a model generated based on an image, and the output of the generative model can include text, image, or a combination of the two. Of course, the input or output of the generative model can also be data of other modalities, such as audio, video, or a combination of multiple types of data. A generative model can be a single-modal model, such as a model that generates text from text (referred to as a "text-to-text model") or a model that generates images from images (referred to as a "image-to-image model"). Alternatively, a generative model can be a cross-modal model, that is, a model whose input and output belong to different modalities, such as a model that generates images from text (referred to as a "text-to-image model"). Alternatively, the input of a generative model can include multiple modalities, and the output can also include multiple modalities.

[0037] For example, after receiving input information, the robot processes it as needed and then feeds it into a machine learning model. The robot then obtains the output of the machine learning model and processes it as needed to generate the robot's output. The robot's output can be displayed directly on the user device's interface or transmitted to other modules in the user device (such as other robots or virtual objects) for further processing.

[0038] The adversarial nature of multiple robots manifests itself in their responses generated based on adversarial configurations. For example, these robots rely on different machine learning models trained with adversarial training data; or, when processing input, these inputs also include adversarial configurations. For example, in the scenario of recommending mobile phones to users, one robot might prioritize performance, while another might prioritize price.

[0039] After receiving user input, each robot can first process the user's input and generate its own message (i.e., the message in the conversation). Alternatively, each robot can also process the user's input and the responses generated by other robots to generate its own response. Each robot's response can be visible to the user or transmitted only between robots. In other words, the "chat" between robots can be presented in a visual manner to show the user the game process between robots; or the conversation process between robots can be hidden, and only the response results after the robots reach a consensus are presented to the user.

[0040] In step S104, conflict detection is performed on the dialogues of the multiple robots to determine conflicting dialogues and conflict types.

[0041] Because multiple robots are adversarial, conversations between them may involve conflicts. If these conflicts remain unresolved, the robots may even reach a stalemate, resulting in no progress in the substantive content of the conversation. Consequently, users are unable to receive timely and effective responses to their input.

[0042] When performing conflict detection, after semantically analyzing the conversations between robots, key information can be extracted. This key information can reflect the key content of one or more messages from a single robot, or it can summarize the conversations between multiple robots, or it can be a key information sequence composed of multiple extracted key information. The key information and key information sequence are then matched with pre-set conflict detection information to determine whether a conflict exists. Alternatively, the conversations between robots can be directly matched with the conflict detection information to determine whether a conflict exists. Other conflict detection methods can also be used by those skilled in the art as needed, and will not be described in detail here.

[0043] In some embodiments, a conflict controller can be used to monitor the robot's conversations. For example, a prompt for conflict detection can be input to the controller to instruct the conflict controller to identify conflicting conversations and conflict types. The prompt can include conflict detection information. The conflict detection information can include, for example, negative conversation examples and their corresponding conflict types. Negative conversation examples are examples of conversations that have experienced conflict.

[0044] In step S106, an adjustment instruction is sent to at least one robot involved in the conflict, so that each adjusted robot continues to communicate with other robots based on the adjustment instruction, where the adjustment instruction is generated based on the user input and the conflict type.

[0045] Adjustment instructions are used to notify the robot to adjust information such as message generation methods and content. The robot can adjust its settings accordingly or temporarily add the adjustment instructions to its settings. The robot can process the adjustment instructions to update the dialogue strategy. For example, the adjustment instructions or content determined based on the adjustment instructions can be input into a machine learning model to obtain the output of the machine learning model.

[0046] When a conflict occurs, all or some of the robots involved in the conflicting conversation can be identified as robots to be adjusted. That is, adjustments can be made to all or some of the robots involved. For example, some robots can be instructed to make concessions, or all robots can be instructed to back down. In some embodiments, the dimensions involved in the conflict type can be determined, and the robots can be instructed to make opposing adjustments along those dimensions. For example, if robots are deadlocked over price, the robot insisting on a lower price can be instructed to raise its bottom line.

[0047] In some embodiments, the adjustment instructions also include conflicting conversations. This more clearly identifies problematic conversations, helping the robot make more accurate adjustments. However, since each robot can receive messages from other robots and store its own messages, it is not necessary to send conflicting conversations to the robot again.

[0048] In step S108 , in response to the dialogues of the plurality of robots including a response to the user's input and the dialogues of the plurality of robots having no conflict, a response is displayed.

[0049] Based on user input, a robot may engage in multiple rounds of dialogue, and the first round of dialogue may not necessarily fully respond to the user's input. For example, for a user-input task of recommending mobile phones, multiple robots may initially identify candidate models and then further narrow them down through discussion. Semantic analysis can be used to determine whether a robot's dialogue is a response to the user's input—that is, whether the robot has completed the communication and discussion related to the user's input.

[0050] If the robot's conversation includes a response to a user's input, the conflict detection method described in the above steps can be used to further determine whether there is a conflict. If there is no conflict, it means that the robots have reached an agreement, and the robot's response can be displayed to the user.

[0051] The above embodiment utilizes multiple robots with adversarial relationships to jointly develop responses to user inputs, enabling these responses to be considered across multiple dimensions and resulting in higher reliability. However, due to the adversarial nature of the robots, conflicts may arise during the conversation. In the event of a conflict or even a stalemate in the robot conversation, adjustment instructions can be sent to at least one of the robots involved in the conflict, allowing the conversation between the robots to continue, thereby improving the efficiency of user responses.

[0052] In some embodiments, user input involves multiple dimensions, and each of the multiple robots generates a conversation based on some or all of these dimensions. Different robots may have overlapping dimensions, but their dimensions are not completely identical, creating a competitive environment between the robots. For example, Robot A's dimensions may include high performance and attractive appearance, while Robot B's dimensions may include low price and attractive appearance. Both robots possess the dimension of "attractive appearance," but are competitive in terms of performance and price.

[0053] When performing conflict detection, the conflict detection can be performed based on the dimensions involved in the robot's dialogue. An embodiment of the conflict detection method disclosed herein is described below with reference to FIG2 .

[0054] Figure 2 shows a schematic flow chart of a conflict detection method according to some embodiments of the present disclosure. As shown in Figure 2 , the conflict detection method of this embodiment includes steps S202 to S204.

[0055] In step S202, the dimensions involved in the dialogues between the multiple robots are determined. For example, semantic analysis can be performed on the dialogues between the robots to determine key information of the dialogues, and the key information is matched with the multiple dimensions involved in the user input to determine the dimensions involved in the dialogues.

[0056] Due to the adversarial nature between robots, a message sent by one robot could easily contain conflicting dimensions with a message sent by another robot. However, some robots will adjust themselves during multiple rounds of interaction. Therefore, in some embodiments, based on multiple rounds of dialogue between robots, the dimensions corresponding to each robot in these rounds can be analyzed holistically. For example, if a robot insists on a low price at the beginning of a conversation but makes concessions on the price during multiple rounds of interaction, the robot's conversation no longer involves the low price dimension.

[0057] In some embodiments, based on a conversation between multiple robots, at least one of the topic of the conversation, the flow of the conversation, and the interaction pattern between the multiple robots is determined; and the dimensions involved in at least one of the topic of the conversation, the flow of the conversation, and the interaction pattern between the multiple robots are determined as the dimensions involved in the conversation between the multiple robots. For example, semantic analysis of the robot conversation can be performed to obtain any one of the topic, the flow of the conversation, and the interaction pattern between the robots, and then the dimension can be matched with a preset template for each dimension of the topic, the flow of the conversation, and the interaction pattern between the robots to determine the dimensions involved in the conversation.

[0058] The topic of a conversation refers to the core content of the conversation, derived from analyzing the topic. The flow of a conversation refers to the process of analyzing and processing user input. For example, when recommending a mobile phone to a user, you can first lock in a large number of alternative phone models and then choose from them, or you can first have each robot recommend a model, and then find a more suitable alternative based on multiple rounds of dialogue between robots. The interaction mode refers to whether robots are divided into different tasks, divided into groups with each group handling different tasks, or collectively handling a task. Different interaction modes can be used at different stages of processing user input. Conflicts in any of the topics, the flow of the conversation, or the interaction mode between robots may prevent the conversation from moving forward.

[0059] In step S204 , in response to the fact that the dimensions involved in the conversation among the multiple robots include conflicting dimensions, it is determined that the conversation among the multiple robots has a conflict.

[0060] After determining the existence of a conflict, the conflict type can be further determined. For example, the conflict type of the conversation can be determined based on the type of dimension in which the conflict occurs. Conflict types can include topic conflicts, conversation flow conflicts, and conflicts in the interaction patterns between robots. These types can also be further subdivided into the above three types. Of course, conflict types can also be categorized in other ways, which will not be detailed here.

[0061] Through the above embodiment, the dimensions involved in the robot's conversation can be extracted, and whether there is a conflict in the conversation can be determined based on the conflict between the dimensions. Therefore, conflict detection can be performed more accurately.

[0062] In addition to performing conflict detection based on the dimensions involved in the conversation, negative conversation examples of the conflict may also be predetermined to perform conflict detection based on the negative conversation examples. A conflict detection method according to another embodiment of the present disclosure is described below with reference to FIG3 .

[0063] Figure 3 shows a schematic flow chart of a conflict detection method according to some other embodiments of the present disclosure. As shown in Figure 3 , the conflict detection method of this embodiment includes steps S302 to S304.

[0064] In step S302 , the conversations of the multiple robots are matched with negative conversation examples, where the negative conversation examples correspond to each of the multiple candidate conflict types.

[0065] Negative dialogue examples are conflicting examples. They can be examples from other dialogue scenarios collected and set before the robot conversation begins, or examples obtained through other means. Negative dialogue examples can be the dialogue text itself or abstracted from the negative dialogue text. For example, negative dialogue examples include "Robot T refuses to adjust its strategy even when it clearly conflicts with Robot U's goals" and "Robot T ignores the importance of shared resources, resulting in inefficiency."

[0066] In step S304, based on the matching results of the conversations of the multiple robots and the negative conversation examples, conflicting conversations and conflict types in the conversations between the multiple robots are determined.

[0067] When matching, the original text of the robot's conversation can be matched with negative conversation examples. This method has a relatively high recognition accuracy. Alternatively, the original text of the robot's conversation can be semantically analyzed and abstracted to extract the main content of the conversation, and then matched with negative conversation examples. This method can improve the matching hit rate.

[0068] In some embodiments, the machine learning model can also be used for processing. For example, the conversation between the robots and the prompt are input into a conflict detection controller based on the machine learning model. The prompt can include an instruction to perform conflict detection on the conversation between the robots, as well as negative conversation examples. Thus, the machine learning model can process the conversation between the robots based on the prompt, and by referring to the negative conversation examples and leveraging the powerful semantic understanding and analysis capabilities of the machine learning model, efficiently and accurately detect conflicts and conflict types in the conversation.

[0069] Through the above embodiments, conflict detection can be performed based on negative dialogue examples, which helps to accurately detect and analyze conflict information in detail, and improves the efficiency of subsequent conflict resolution.

[0070] As previously mentioned, when instructing robots to make adjustments, all robots involved in the conflicting conversations can be identified as robots to be adjusted, or some robots involved in the conflicting conversations can be identified as robots to be adjusted. When determining the robots to be adjusted, some robots involved in the conflicting conversations can be further identified as robots to be adjusted based on user input, the conflict type, and the adjustment priorities of the multiple robots. Adjustment instructions can then be sent to the robots to be adjusted.

[0071] Based on the user's input, it's possible to determine which robot's configuration information (e.g., the dimensions covered by the robot) is most relevant to the user's input. In other words, the robot to be adjusted can be determined based on the correlation between the robot's configuration information and the user's input. For example, if the user input is "Recommend me a mobile phone that is easy to use and preferably lasts longer," while this input involves both price and performance, the phrase "preferably lasts longer" indicates that the user prioritizes performance. Therefore, in the event of a conflict, the robot making recommendations based on price can be instructed to compromise and make concessions.

[0072] Based on the conflict type, you can determine which robot is the source of the conflict. For example, if a conflict occurs between robots A and B because robot A doesn't share information, the conflict type is "Information Not Shared." This conflict type is caused by robots that don't share information, so robot A, the source of the conflict, can be selected as the robot to be adjusted.

[0073] Based on the adjustment priorities of multiple robots, it is possible to determine which robot should be instructed to make adjustments first when a conflict occurs. This priority information can be pre-set or dynamically assigned during the robot interaction process.

[0074] By using the above methods to determine the robot to be adjusted, a feasible solution to the conflict can be provided more efficiently, thereby improving the efficiency of conflict resolution and thus improving the efficiency of responding to users.

[0075] In some embodiments, an adjustment instruction is generated based on the user's input, the conflict type, and a positive dialogue example corresponding to the conflict type. A positive dialogue example refers to an example in which a conflict exists during a dialogue but is resolved as the dialogue progresses. It can be an example in other dialogue scenarios that are pre-collected and set before the robot's dialogue begins, or an example obtained by other means. The positive dialogue example can be the dialogue text itself, or it can be content abstracted from the positive dialogue text. Through the positive dialogue example, the generated adjustment instruction can be made more reasonable and feasible. The following describes an embodiment of the method for generating adjustment instructions of the present disclosure with reference to FIG4 .

[0076] Figure 4 shows a flow chart of a method for generating an adjustment instruction according to some embodiments of the present disclosure. As shown in Figure 4 , the method for generating an adjustment instruction in this embodiment includes steps S402 to S404.

[0077] In step S402, positive historical conversation examples are identified from historical conversations between multiple robots that can be used to resolve conflicts of the conflict type. Specifically, solutions to similar conflicts encountered by multiple robots during the current task are used as a reference for the current conflict. Because positive historical conversation examples are generated by the current robot, adjustments to the robot causing the conflict can be made more effectively.

[0078] In step S404, an adjustment instruction for the first robot is generated based on the task, the conflict type, and the positive dialogue examples and positive historical dialogue examples corresponding to the conflict type, where the adjustment instruction includes the positive historical dialogue example.

[0079] Adjustment instructions are used to instruct the robot how to adjust. They can be specific adjustment strategies, such as "making concessions on price" or "sharing known background information with other robots." They can also be positive historical dialogue examples, positive dialogue examples, or both.

[0080] The above embodiment improves the robot's adjustment efficiency by using positive historical dialogue examples as a basis for adjustment when instructing the robot to adjust, thereby improving the efficiency of the robot's response to user input.

[0081] The following describes several methods for generating adjustment instructions for example with respect to several conflict types, namely, goal conflict, policy conflict, information understanding conflict, and resource allocation conflict.

[0082] In some embodiments, the conflict type includes a target inconsistency. Generating an adjustment instruction includes: determining adjustment information for the target of the first robot based on user input, the conflict type and an example corresponding to the conflict type, and the targets of the first robot and the second robot involved in the conflict; and generating an adjustment instruction for the first robot based on the adjustment information. The number of the first and second robots can be one or more.

[0083] A bot's goal can be understood as something determined by the bot based on user input and its settings. For example, if the user input is "Recommend a phone for me," and the bot prefers to recommend low-priced phones, the bot's initial goal would be "Recommend a phone for me, priced no more than xx." This goal may be slightly adjusted as the conversation progresses.

[0084] The target adjustment information may include at least one of an adjustment direction and an adjustment amplitude, to instruct the first robot to compromise to partially achieve its original target; or, may include an adjusted target, to instruct the first robot to redefine the target.

[0085] This embodiment provides adjustment instructions, including goal adjustment information, to instruct robots to directly resolve issues that arise, thereby balancing different goals. By first analyzing the conflict points between adversarial digital humans and then proposing specific solutions, conflicts between robots can be reduced and the efficiency of user response can be improved.

[0086] In some embodiments, the conflict type includes policy inconsistency. Generating an adjustment instruction includes: generating a new policy based on the original policies of the multiple robots in response to the user input, the conflict type, and examples corresponding to the conflict type; and generating an adjustment instruction based on the new policy. A policy refers to the operational strategy adopted by the robot in response to the user input. The actions performed based on the operational strategy are reflected as the flow of the conversation. For example, a policy can be a series of steps required to respond to the user, or information to be obtained or generated in different situations.

[0087] When conflicts arise among the strategies of robots, new strategies can be generated and instructing the robots to execute them, thereby unifying the strategies among the robots and reducing conflicts among the robots and improving the efficiency of responding to users.

[0088] In some embodiments, the conflict type includes a conflict in information understanding. Generating an adjustment instruction includes: determining the background information involved in the conflicting conversation; based on the user's input, the conflict type, and an example corresponding to the conflict type, generating an instruction to provide background information, or an instruction to accept background information as an adjustment instruction. Conflicts in information understanding include, for example, differences in information understanding or misunderstandings. Since different robots have different setting information, different robots may rely on different information sources or background knowledge when generating messages. Adjustment instructions can be provided to instruct the robots to merge their respective information in order to eliminate conflicts in information understanding. By providing an instruction for background information, the robot can actively send information that the other party does not have; by accepting an instruction for background information, the robot can regard the information provided by the other party as part of its own information.

[0089] By promoting knowledge fusion, different robots can be instructed to integrate each other's information and views, thereby promoting each robot to make more comprehensive decisions and improving the reliability and availability of responses to users.

[0090] In some embodiments, the conflict type includes a resource allocation conflict. Generating the adjustment instruction includes: generating details of setting information for the plurality of robots based on the tasks, the conflict type, and examples corresponding to the conflict type; generating a resource sharing strategy based on the details of the setting information for the plurality of robots; and generating the adjustment instruction based on the details of the setting information for the plurality of robots and the resource sharing strategy.

[0091] Resource conflicts and robot settings are often interrelated. For example, in resource allocation scenarios, different roles may encounter conflicts due to varying resource requirements. Differences in settings can also affect resource allocation and utilization. By referencing robot settings when generating resource sharing strategies and adjustment instructions, we can more comprehensively resolve robot dialogue conflicts.

[0092] Therefore, by generating detailed robot configuration information based on conflict types, we can further refine the original robot configuration information to help clarify and optimize the roles and responsibilities of different robots. Furthermore, by generating resource sharing strategies, we can balance the fairness and efficiency of resource allocation, thereby improving the overall efficiency of robot response generation.

[0093] In some embodiments of the present disclosure, the conversation process between multiple robots can be invisible or visible to the user. Adjustment instructions for the robots can be invisible or visible to the user. This is described below with reference to Figures 5A to 5C.

[0094] Figure 5A shows a schematic diagram of a user interface according to some embodiments of the present disclosure. As shown in Figure 5A, the interface 51 includes a dialogue between a user 511 and an intelligent virtual object 512. The user inputs "Please recommend a mobile phone to me." After receiving the user's input, the background of the application where the interface 51 is located (which can be a terminal or a server) calls multiple adversarial robots 513, 514, and 515 to conduct a "dialogue" that is invisible to the user. During the "dialogue" process, the conflict controller 516 located in the background performs conflict detection and sends adjustment instructions to the robots when a conflict is detected. After the robots 53, 54, and 55 reach an agreement, the result "X brand mobile phone Y is good" is fed back to the user through the intelligent virtual object 52 in the foreground. This method can directly feedback the results to the user, thereby improving the efficiency of the user in receiving effective information.

[0095] Figure 5B illustrates a user interface diagram according to other embodiments of the present disclosure. As shown in Figure 5B, interface 52 includes a conversation between user 521 and multiple robots 523, 524, and 525. The conversations between the robots are visible to the user. Simultaneously, a conflict controller 525 in the background monitors the conversations between the robots and performs conflict detection. Based on the detection results, conflict controller 525 discovers that robot 523 is overly insistent on its specific phone performance requirements and refuses to compromise on the recommendations of other robots, leading to a deadlock in the conversation. Conflict controller 525 can send an adjustment instruction to robot 523, causing it to relax its performance requirements. After several more rounds of conversation and discussion, robots 523, 524, and 525 unanimously decide to recommend phone Y, brand X, thus providing a conflict-free response to the user's input. This approach presents the discussion process between the robots to the user, making it easier for the user to obtain more relevant information.

[0096] Figure 5C illustrates a user interface diagram according to yet another embodiment of the present disclosure. As shown in Figure 5C, interface 53 includes a conversation between user 531 and multiple robots 533 (Robot 1), 534 (Robot 2), and 535 (Robot 3). The conversations between the robots are visible to the user. Meanwhile, conflict controller 535, as a participant in the group chat, sends a message within the group chat to issue adjustment instructions when a conflict is detected. Based on the detection results, conflict controller 535 finds that robot 533 is overly insistent on its phone performance requirements and completely refuses to compromise on the recommendations of other robots, leading to a deadlock in the conversation. Conflict controller 535 can send an instruction to robot 2 within the group chat to relax the performance standards, guiding the conversation smoothly until the three robots reach an agreement. This approach presents the discussion and adjustment process between the robots to the user, making it easier for the user to obtain more relevant information and allowing the user to better understand the recommendation logic of each robot, providing further reference for the final decision.

[0097] The above describes the robot control method of some embodiments of the present disclosure. The following describes embodiments of related devices of the present disclosure in conjunction with other drawings.

[0098] FIG6 is a schematic diagram of a robot control device according to some embodiments of the present disclosure. As shown in FIG6 , the robot control device 60 of this embodiment includes: an acquisition module 601 configured to, in response to user input, acquire a dialogue between multiple robots in response to the user input, wherein the multiple robots are antagonistic; a detection module 602 configured to perform conflict detection on the dialogues between the multiple robots to determine conflicting dialogues and conflict types; a sending module 603 configured to send an adjustment instruction to at least one robot involved in the conflict, so that each adjusted robot continues to communicate with the other robots based on the adjustment instruction, wherein the adjustment instruction is generated based on the user input and the conflict type; and a display module 604 configured to display the response in response to the dialogue between the multiple robots including a response to the user input and the absence of conflict in the dialogue between the multiple robots.

[0099] In some embodiments, the user's input involves multiple dimensions, and each of the multiple robots generates a dialogue based on some or all of the multiple dimensions.

[0100] In some embodiments, the detection module 602 is further configured to determine dimensions involved in the conversations among the multiple robots; and in response to the dimensions involved in the conversations among the multiple robots including conflicting dimensions, determine that the conversations among the multiple robots are in conflict.

[0101] In some embodiments, the detection module 602 is further configured to determine at least one of the topic of the conversation, the flow of the conversation, and the interaction mode between the multiple robots based on the conversation between the multiple robots; and determine the dimensions involved in at least one of the topic of the conversation, the flow of the conversation, and the interaction mode between the multiple robots as the dimensions involved in the conversation between the multiple robots.

[0102] In some embodiments, the detection module 602 is further configured to determine conflicting conversations and conflict types in the conversations between the multiple robots based on the matching results of the conversations of the multiple robots with negative conversation examples, wherein the negative conversation examples correspond to each of the multiple candidate conflict types.

[0103] In some embodiments, the sending module 603 is further configured to: determine all robots involved in the conflicting conversation as robots to be adjusted, or, based on user input, conflict type, and adjustment priority of multiple robots, determine some robots from the robots involved in the conflicting conversation as robots to be adjusted; and send adjustment instructions to the robots to be adjusted.

[0104] In some embodiments, the control device 60 of the robot further includes a generation module 605 configured to generate an adjustment instruction.

[0105] In some embodiments, the generation module 605 is further configured to generate an adjustment instruction based on the user's input, the conflict type, and a positive dialogue example corresponding to the conflict type.

[0106] In some embodiments, the generation module 605 is further configured to determine positive historical dialogue examples for resolving conflicts of the conflict type in historical dialogues between multiple robots; based on the task, the conflict type, and the positive dialogue examples and positive historical dialogue examples corresponding to the conflict type, generate an adjustment indication for the first robot, the adjustment indication including the positive historical dialogue example.

[0107] In some embodiments, the conflict type includes target inconsistency, and the generation module 605 is further configured to determine adjustment information of the target of the first robot based on user input, the conflict type and an example corresponding to the conflict type, and the targets of the first robot and the second robot involved in the conflict; and generate adjustment instructions for the first robot based on the adjustment information.

[0108] In some embodiments, the conflict type includes policy inconsistency, and the generation module 605 is further configured to generate a new policy based on the original policies of multiple robots for user input, the conflict type, and examples corresponding to the conflict type; and generate an adjustment instruction based on the new policy.

[0109] In some embodiments, the conflict type includes a conflict in information understanding, and the generation module 605 is further configured to determine the background information involved in the conflicting conversation; based on the user input, the conflict type, and examples corresponding to the conflict type, an indication to provide background information or an indication to accept background information is generated as an adjustment indication.

[0110] In some embodiments, the conflict type includes a conflict of resource allocation, and the generation module 605 is further configured to generate details of setting information of multiple robots based on the task, conflict type and examples corresponding to the conflict type; generate a resource sharing strategy based on the details of the setting information of multiple robots; and generate adjustment instructions based on the details of the setting information of multiple robots and the resource sharing strategy.

[0111] In some embodiments, the adjustment indication also includes the presence of conflicting conversations.

[0112] It should be noted that the above-mentioned units are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods. For example, they can be implemented in software, hardware, or a combination of software and hardware. In actual implementation, the above-mentioned units can be implemented as independent physical entities, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.). In addition, the above-mentioned units are shown with dotted lines in the accompanying drawings to indicate that these units may not actually exist, and the operations / functions they implement can be implemented by the processing circuit itself.

[0113] In addition, although not shown, the device may also include a memory that can store various information generated by the device and the various units contained in the device during operation, programs and data used for operation, data to be sent by the communication unit, etc. The memory can be volatile memory and / or non-volatile memory. For example, the memory can include but is not limited to random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and flash memory. Of course, the memory can also be located outside the device. Optionally, although not shown, the device may also include a communication unit that can be used to communicate with other devices. In one example, the communication unit can be implemented in an appropriate manner known in the art, for example, including communication components such as an antenna array and / or a radio frequency link, various types of interfaces, communication units, etc. This will not be described in detail here. In addition, the device may also include other components not shown, such as a radio frequency link, a baseband processing unit, a network interface, a processor, a controller, etc. This will not be described in detail here.

[0114] Some embodiments of the present disclosure also provide an electronic device. Figure 7 shows a schematic structural diagram of an electronic device according to some embodiments of the present disclosure. For example, in some embodiments, the electronic device 7 can be various types of devices, for example, including but not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. For example, the electronic device 7 may include a display panel for displaying data and / or execution results utilized in the scheme of the present disclosure. For example, the display panel can be of various shapes, such as a rectangular panel, an elliptical panel, or a polygonal panel. In addition, the display panel can be not only a flat panel, but also a curved panel or even a spherical panel.

[0115] As shown in FIG7 , the electronic device 7 of this embodiment includes a memory 71 and a processor 72 coupled to the memory 71. It should be noted that the components of the electronic device 7 shown in FIG7 are merely exemplary and non-limiting. The electronic device 7 may also include other components as required by actual applications. The processor 72 may control the other components in the electronic device 7 to perform desired functions.

[0116] In some embodiments, the memory 71 is configured to store one or more computer-readable instructions. When the processor 72 is configured to execute the computer-readable instructions, the computer-readable instructions are executed by the processor 72 to implement the method according to any of the above-described embodiments. The specific implementation and related explanations of each step of the method can be found in the above-described embodiments, and any repetitive details are omitted here.

[0117] For example, the processor 72 and the memory 71 may communicate with each other directly or indirectly. For example, the processor 72 and the memory 71 may communicate with each other via a network. The network may include a wireless network, a wired network, and / or any combination of wireless networks and wired networks. The processor 72 and the memory 71 may also communicate with each other via a system bus, which is not limited in this disclosure.

[0118] For example, the processor 72 can be embodied as various appropriate processors, processing devices, etc., such as a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The central processing unit (CPU) can be an X86 or ARM architecture, etc. For example, the memory 71 can include any combination of various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The memory 71 can include, for example, a system memory, which stores, for example, an operating system, an application, a boot loader (Boot Loader), a database, and other programs. Various applications and various data can also be stored in the storage medium.

[0119] In addition, according to some embodiments of the present disclosure, when various operations / processes according to the present disclosure are implemented through software and / or firmware, the programs constituting the software can be installed from a storage medium or a network to a computer system having a dedicated hardware structure, such as the computer system 80 shown in FIG8 . When the various programs are installed, the computer system can perform various functions, including those described above. FIG8 shows a schematic structural diagram of a computer system according to some embodiments of the present disclosure.

[0120] In Figure 8, a central processing unit (CPU) 801 performs various processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage section 808 to a random access memory (RAM) 803. In the RAM 803, data required when the CPU 801 performs various processes, etc., is also stored as needed. The central processing unit is merely exemplary and may also be other types of processors, such as the various processors described above. The ROM 802, RAM 803, and storage section 808 may be various forms of computer-readable storage media, as described below. It should be noted that although ROM 802, RAM 803, and storage device 808 are shown separately in Figure 8, one or more of them may be combined or located in the same or different memory or storage modules.

[0121] The CPU 801, the ROM 802, and the RAM 803 are connected to one another via a bus 804. An input / output interface 805 is also connected to the bus 804.

[0122] The following components are connected to the input / output interface 805: an input portion 806, such as a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output portion 807, including a display, such as a cathode ray tube (CRT), liquid crystal display (LCD), speaker, vibrator, etc.; a storage portion 808, including a hard disk, magnetic tape, etc.; and a communication portion 809, including a network interface card, such as a LAN card, modem, etc. The communication portion 809 allows communication processing to be performed via a network, such as the Internet. It will be readily understood that although FIG8 shows that the various devices or modules in the computer system 80 communicate via bus 804, they may also communicate via a network or other means, where the network may include a wireless network, a wired network, and / or any combination of wireless and wired networks.

[0123] A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 810 as needed so that a computer program read therefrom is installed in the storage section 808 as needed.

[0124] In the case of realizing the above-described series of processing by software, a program constituting the software can be installed from a network such as the Internet or a storage medium such as the removable medium 811 .

[0125] According to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the CPU 801, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0126] It should be noted that in the context of the present disclosure, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0127] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0128] In some embodiments, a computer program is further provided, comprising: instructions, which, when executed by a processor, cause the processor to perform any of the methods of the above embodiments. For example, the instructions may be embodied as computer program codes.

[0129] In embodiments of the present disclosure, computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0131] The modules, components, or units described in the embodiments of the present disclosure may be implemented in software or hardware. The names of the modules, components, or units do not necessarily limit the modules, components, or units themselves.

[0132] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, and without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0133] The above descriptions are merely some embodiments of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present disclosure.

[0134] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present invention may be practiced without these specific details. In other cases, well-known methods, structures, and techniques are not presented in detail in order not to obscure the understanding of the description.

[0135] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0136] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art will appreciate that the above examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A robot control method, comprising: In response to a user input, obtaining a dialogue between multiple robots regarding the user input, wherein the multiple robots are antagonistic to each other; Performing conflict detection on the conversations of the multiple robots to determine conflicting conversations and conflict types; Sending an adjustment instruction to at least one robot involved in the conflict, so that each adjusted robot continues to communicate with other robots based on the adjustment instruction, wherein the adjustment instruction is generated based on the user input and the conflict type; In response to the conversations among the plurality of robots including a response to the user's input and the conversations among the plurality of robots having no conflict, the response is displayed.

2. The control method according to claim 1, wherein: The user's input involves multiple dimensions, and each of the multiple robots generates a dialogue based on part or all of the multiple dimensions.

3. The control method according to claim 1 or 2, wherein: The performing conflict detection on the dialogues of the multiple robots includes: determining dimensions involved in the conversation among the plurality of robots; In response to the dimensions involved in the conversation among the multiple robots including a conflicting dimension, it is determined that a conflict exists in the conversation among the multiple robots.

4. The control method according to claim 3, wherein: The dimensions involved in determining the dialogues among the multiple robots include: Determining, based on the conversation among the multiple robots, at least one of a topic of the conversation, a flow of the conversation, and an interaction mode among the multiple robots; The dimensions involved in at least one of the topic of the conversation, the flow of the conversation, and the interaction mode between the multiple robots are determined as the dimensions involved in the conversation between the multiple robots.

5. The control method according to any one of claims 1 to 4, wherein: The performing conflict detection on the dialogues of the multiple robots to determine conflicting dialogues and conflict types includes: Based on the matching results of the conversations of the multiple robots and the negative conversation examples, the conflicting conversations and the conflict types in the conversations between the multiple robots are determined, wherein the negative conversation examples correspond to each of a plurality of candidate conflict types.

6. The control method according to any one of claims 1 to 5, wherein: The sending of an adjustment instruction to at least one robot involved in the conflict includes: Determining all robots involved in the conflicting conversations as robots to be adjusted, or, based on the user input, the conflict type, and the adjustment priorities of the multiple robots, determining some robots from the robots involved in the conflicting conversations as robots to be adjusted; Sending an adjustment instruction to the robot to be adjusted.

7. The control method according to any one of claims 1 to 6, further comprising: The adjustment instruction is generated based on the user input, the conflict type, and a positive dialogue example corresponding to the conflict type.

8. The control method according to claim 7, wherein: Generating the adjustment instruction includes: determining, from among the historical conversations between the plurality of robots, examples of positive historical conversations for resolving conflicts of the conflict type; Based on the task, the conflict type, and the positive conversation examples corresponding to the conflict type and the positive historical conversation examples, an adjustment instruction for the first robot is generated, the adjustment instruction including the positive historical conversation examples.

9. The control method according to any one of claims 1 to 8, wherein: The conflict type includes inconsistent goals, and the control method further includes: determining adjustment information for the target of the first robot based on the user input, the conflict type and an example corresponding to the conflict type, and targets of the first robot and the second robot involved in the conflict; Based on the adjustment information, an adjustment instruction for the first robot is generated.

10. The control method according to any one of claims 1 to 9, wherein: The conflict type includes policy inconsistency, and the control method further includes: generating a new strategy based on the original strategies of the plurality of robots in response to the user's input, the conflict type, and examples corresponding to the conflict type; Based on the new policy, the adjustment instruction is generated.

11. The control method according to any one of claims 1 to 10, wherein: The conflict type includes a conflict of information understanding, and the control method further includes: Determining the context of the conflicting conversation; Based on the user input, the conflict type, and an example corresponding to the conflict type, an instruction to provide the context information or an instruction to accept the context information is generated as the adjustment instruction.

12. The control method according to any one of claims 1 to 11, wherein: The conflict type includes a resource allocation conflict, and the control method further includes: generating details of setting information of the plurality of robots based on the tasks, the conflict types, and examples corresponding to the conflict types; generating a resource sharing strategy based on details of the setting information of the plurality of robots; The adjustment instruction is generated based on the details of the setting information of the plurality of robots and the resource sharing policy.

13. The control method according to any one of claims 1 to 12, wherein: The adjustment instruction also includes the conflicting dialogue.

14. A robot control device comprising: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the robot control method according to any one of claims 1 to 13 based on instructions stored in the memory.

15. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the robot control method according to any one of claims 1 to 13 is implemented. 16 . A computer program product, which, when executed on a computer, enables the computer to implement the robot control method according to claim 1 .

17. A computer program comprising: Instructions, when executed by a processor, cause the processor to execute the robot control method according to any one of claims 1 to 13.

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