An Embodied Intelligence Generation Method and System Based on an N-Grid Progressive Mechanism

Abstract

This invention discloses a method and system for generating embodied intelligence based on an N-grid progressive mechanism, relating to the fields of artificial intelligence and multimodal generation technology. The method includes parsing natural language to generate standardized text prompts; generating standardized two-dimensional images; reconstructing standardized three-dimensional models; defining joint motion parameters to generate dynamic three-dimensional models; establishing a mapping between semantic / speech and facial driving parameters to generate an expression control model; integrating a speech module and aligning it with the expression model to generate a multimodal interaction model; mapping virtual actions to entity driving units based on joint motion parameters to generate motion control structures; reasoning and decomposing task instructions to generate physical action sequences and driving their execution; integrating the outputs of each step and coordinating them through a multimodal control center to form a complete embodied intelligent agent. This invention solves the problems of modal fragmentation, link interruption, and capability fragmentation in multimodal generation.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence and multimodal generation technology, and particularly relates to an embodied intelligence generation method and system based on an N-grid progressive mechanism. This invention achieves a unified methodological system from abstract language description to embodied intelligent agent generation through multimodal progressive evolution including language, vision, speech, and action. Background Technology

[0002] With the rapid development of large-scale models, intelligent robots, and multimodal generation technologies, how to organically integrate multiple capabilities such as language understanding, visual perception, voice interaction, and motion control into a unified embodied intelligent agent has become a research hotspot and core challenge in the field of artificial intelligence. Embodied intelligent agents not only need to possess multimodal perception and generation capabilities, but also need to achieve a closed-loop process from cognition to action in order to perform complex tasks in real physical environments.

[0003] Currently, multimodal generation technology has made some progress. For example, modules such as text-to-image generation, image-to-3D model generation, and speech synthesis and recognition can operate independently and produce high-quality output. However, existing systems still have significant shortcomings in integrating multimodal capabilities, specifically: (1) Lack of progressive structured generation process: Each modal generation process is usually designed independently and called in sequence, lacking a unified evolutionary logic among them. For example, text to image and image to 3D model are often treated as isolated tasks, without forming a progressive link from abstract semantics to specific structure and then to dynamic behavior, which makes it difficult for the generation results to be naturally connected.

[0004] (2) Inconsistent output specifications between modalities: Due to the lack of standardized conventions for the output format, semantic representation and data structure of different generation modules, the output of the previous stage cannot be directly used as the input of the next stage, which makes it difficult to align cross-modal information, the generation chain is easily interrupted, and hinders the continuous construction of multimodal capabilities.

[0005] (3) Separation of cognitive and executive capabilities: Most existing technologies remain at the level of generating single modalities or static content, making it difficult to start from natural language descriptions and progressively generate complete intelligent agents with three-dimensional structure, joint mobility, facial expression interaction and physical execution capabilities. In other words, they lack the full-link capability from language cognition to action execution, and cannot meet the integrated requirements of "understanding-interaction-action" required by embodied intelligent agents.

[0006] In summary, current technologies cannot support the construction of embodied intelligent agents capable of performing tasks by starting with natural language descriptions and using standardized, progressive multimodal generation methods. Therefore, a new multimodal generation mechanism is urgently needed to enable each generation stage to connect and advance layer by layer, ultimately forming an embodied intelligent system with complete cognitive, interactive, and executive capabilities. Summary of the Invention

[0007] To address the aforementioned deficiencies in existing technologies, the present invention aims to provide an embodied intelligent generation method and system based on an N-grid progressive mechanism. This method solves the problems of lack of structured progression in the multimodal generation process, inability to standardize and align cross-modal outputs, and lack of end-to-end capabilities from cognition to execution in existing technologies. It enables the generation of a complete embodied intelligent agent with language understanding, visual presentation, dynamic structure, voice interaction, physical movement, and task execution capabilities, starting from natural language description and progressively generating layer by layer.

[0008] This invention solves the above-mentioned technical problems through the following technical solution: an embodied intelligence generation method based on an N-grid progressive mechanism, comprising: Parse natural language descriptions to generate standardized text prompts; Generate a standardized two-dimensional image based on the standardized text prompts; Based on the normalized two-dimensional image, a standardized three-dimensional model is reconstructed; Define joint motion parameters for the standardized 3D model to generate a dynamic 3D model; Based on the dynamic 3D model, a mapping between semantics or speech and facial geometric structure driving parameters is established to generate an expression control model. The system integrates speech recognition, understanding, and synthesis modules, and aligns them with the facial expression control model to generate a multimodal interaction model. Based on the joint motion parameters in the multimodal interaction model, virtual actions are mapped to entity driving units to generate entity motion control structures. Based on the physical motion control structure, semantic reasoning and decomposition of user task instructions are performed to generate physical action sequences and drive their execution. The outputs of the above steps are integrated into a multimodal control center, forming a complete embodied intelligent agent through data interaction and logical collaboration; The outputs of each step are standardized and used as standardized inputs for subsequent steps, forming a progressive generation chain.

[0009] This invention constructs a complete generative chain from natural language description to embodied intelligent agent through the sequential execution of nine steps, S1 to S9. The output of each step serves as the standardized input for the subsequent steps, and the generation result of the previous step necessarily serves the next, forming a progressive evolutionary relationship. This progressive chain enables the orderly evolution of multimodal capabilities such as language, vision, 3D structure, dynamic control, voice interaction, and entity execution, integrating the originally isolated generation steps into a mutually supportive and collaboratively evolving organic whole, thereby solving the problem of the lack of structured connection in the multimodal generation process in existing technologies. Unlike traditional technologies where each modality is generated independently and in isolation, this invention, through a clear progressive relationship, ensures that the generation result of the previous step necessarily serves the next step, making each modal capability no longer an isolated module, but a mutually supportive and collaboratively evolving organic whole.

[0010] This invention explicitly defines that "the output of each step is standardized and used as the standardized input for subsequent steps," establishing a unified data interface and mapping rules between modalities. Through standardization, it ensures that the "standardized text prompts" output by S1 can be directly parsed by the image generation module of S2; the "standardized 2D image" output by S2 has a clear topological structure and can be directly used by the 3D reconstruction algorithm of S3; the "standardized 3D model" output by S3, after defining joint motion parameters, generates a "dynamic 3D model" that can be directly called by the expression control module of S5 and the motion control module of S7; and so on, achieving unified alignment of data formats, semantic expressions, and structural parameters between steps. Standardization makes the output of the previous step naturally adaptable to the input requirements of the next step, avoiding link interruptions caused by format incompatibility, missing information, or semantic ambiguity, ensuring the consistency and traceability of multimodal information during transmission, and ensuring the continuous operation of the generation process.

[0011] This invention comprehensively covers the entire process from language cognition (S1) → visual presentation (S2-S3) → dynamic structure (S4) → facial expression interaction (S5) → voice communication (S6) → physical movement (S7) → task execution (S8) → integration (S9), ultimately generating a complete embodied intelligent agent capable of performing tasks in real space. This intelligent agent can not only understand user commands through S1 and S8, but also possess a human-like appearance through S2-S3, a movable dynamic structure through S4, perform facial expression interaction through S5, perform voice communication through S6, drive physical joints to complete basic movements through S7, decompose high-level tasks into executable physical action sequences through S8, and finally integrate all capabilities into a complete embodied intelligent agent through S9.

[0012] The "parameter mapping relationship" established by S7 associates virtual joint movements with the physical drive signals of the physical drive unit. Through the "task decomposition" of S8, high-level task instructions (such as "grab the cup") are decomposed into executable physical action sequences such as target detection, moving closer, posture adjustment, grasping, and returning. The established parameter mapping relationship is used to convert the action sequences into physical drive signals to drive the movement of the physical joints, so that the intelligent agent has real physical execution capabilities, rather than just remaining at the virtual simulation level.

[0013] Further, the generation of the normalized two-dimensional image includes: The standardized text prompts are received as input, and the semantic information in the standardized text prompts is mapped to the image generation process using a multimodal alignment algorithm. Based on the normalization constraints, multiple two-dimensional images corresponding to the text semantics are generated. The normalization constraints include the standardization of color, pose, and structural contour, so that the generated two-dimensional image has clear topological structure information and can serve as an effective input standard for subsequent three-dimensional modeling steps. One of the multiple two-dimensional images is selected as the normalized two-dimensional image for subsequent three-dimensional modeling.

[0014] This invention receives standardized text prompts as input, uses a multimodal alignment algorithm to map semantic information to the image generation process, and generates multiple 2D images based on standardized constraints on color, pose, and structural contours, selecting one as the standardized 2D image. This technical solution ensures that the generated 2D image not only highly corresponds to the text semantics but also possesses clear topological structural information, serving as an effective input standard for subsequent 3D modeling steps. This solves the problem of 3D reconstruction difficulties caused by unclear image structure during the semantic-to-visual conversion process, laying an accurate foundation for the subsequent construction of geometrically supported 3D models.

[0015] Furthermore, based on the normalized two-dimensional image, a standardized three-dimensional model is reconstructed, including: The normalized two-dimensional image is subjected to feature analysis to extract texture, pose, and depth information. The extracted texture, pose, and depth information are converted into three-dimensional spatial data using a 3D reconstruction algorithm, and spatial reconstruction is performed to generate an initial 3D model; the initial 3D model includes basic geometry and topology. The initial 3D model is standardized to obtain a standardized 3D model; The standardization process includes uniformly processing the model's proportions, topology, and spatial parameters, and recording the model's shape proportions, structural hierarchy, and surface texture information. The standardized 3D model has topological integrity and facilitates joint definition to meet the needs of subsequent steps in embedding motion parameters at joints, connection points, or key structures.

[0016] This invention analyzes the features of a standardized 2D image to extract texture, pose, and depth information, transforming it into 3D spatial data to generate an initial 3D model. The model's proportions, topology, and spatial parameters are then standardized, recording shape proportions, structural hierarchy, and surface texture information. This technical solution ensures that the generated 3D model not only possesses complete geometric and topological structures but also, through standardization, exhibits topological integrity and facilitates joint definition. This allows subsequent steps to embed motion parameters at joints, connection points, or key structures, thus solving the problem of chaotic model structure and inability to support subsequent dynamic processing during the conversion from 2D images to 3D models.

[0017] Furthermore, joint motion parameters are defined on the standardized 3D model to generate a dynamic 3D model, including: Structural analysis is performed on the standardized 3D model to identify the hierarchical relationships and movable node regions of the model; Define motion nodes in the data structure corresponding to the movable node region; Motion constraints are embedded on the motion nodes, and joint motion parameters are defined to generate a parametric 3D model; A skeleton control chain is introduced into the topology of the parameterized 3D model to establish a mapping relationship from joint motion parameters to geometric deformation, generating a dynamic 3D model with motion degrees of freedom description; the dynamic 3D model includes joint motion parameters, hierarchical dependency relationships and physical constraints, and can directly receive subsequent control signals to realize posture changes and action execution.

[0018] This invention analyzes the structure of a model, identifies hierarchical relationships and movable node regions, defines motion nodes in the data structure, embeds motion constraints and defines joint motion parameters, and finally introduces a skeleton control chain to establish a mapping relationship from joint motion parameters to geometric deformation. This technical solution transforms a static, standardized 3D model into a dynamic 3D model containing joint motion parameters, hierarchical dependencies, and physical constraints, enabling it to directly receive subsequent control signals to achieve posture changes and motion execution. This solves the problem of transforming static geometry into dynamic embodiment, achieving structural-level mobility at the 3D geometric level.

[0019] Furthermore, a skeleton control chain is introduced into the topology of the parametric 3D model to establish a mapping relationship from joint motion parameters to geometric deformation, including: Based on the motion nodes defined in the parametric 3D model, a skeletal system corresponding to the model's geometry is constructed, which contains multiple skeletal nodes. Each skeletal node corresponds to a movable part of the model and is associated with a motion node. The multiple skeletal nodes are organized into a parent-child hierarchical structure, and the motion of the parent node is transmitted to the child node. The geometric mesh of the parameterized 3D model is bound to the bone system, and the weights of each vertex of the geometric mesh are assigned to each bone node. Associate the joint motion parameters with the corresponding bone nodes and set the mapping rules between them; When the joint motion parameters change, the position and orientation of the affected bone nodes are calculated according to the parent-child hierarchical structure. The position of each vertex of the geometric mesh is recalculated in combination with the weights to generate geometric deformation, thereby realizing the mapping relationship from joint motion parameters to geometric deformation.

[0020] This invention constructs a skeletal system corresponding to the model's geometry based on motion nodes. The skeletal nodes are organized into a tree-like parent-child hierarchical structure. Geometric meshes are bound to the skeletal system and vertex weights are assigned. Joint motion parameters are associated with corresponding skeletal nodes and mapping rules are set. When parameters change, geometric deformation is calculated based on the parent-child hierarchical structure and weights. This technical solution achieves a precise mapping from joint motion parameters to geometric deformation, ensuring that when control parameters change, the corresponding geometric mesh of the model can undergo correct deformation or displacement, forming a synergistic logic where a change in one part affects the whole, thus solving the problem of the disconnect between joint motion and geometric deformation in virtual models.

[0021] Furthermore, based on the dynamic 3D model, a mapping between semantics or speech and facial geometric structure driving parameters is established to generate an expression control model, including: Based on the dynamic 3D model, the facial geometry that can be driven is determined; The preset semantic-expression mapping rule library is invoked to map and bind the received semantic signals or voice input with the driving parameters of the facial geometry, thereby establishing a mapping relationship between semantic or voice input and driving parameters; Based on the mapping relationship, semantic signals or voice input are converted into control commands and used to drive the facial geometry. The parameters of the facial geometry are adjusted according to the control command to generate corresponding facial expressions in three-dimensional geometry, and an expression control model is output. The facial expressions include at least one of lip movements, facial expressions, and head posture changes. The expression control model can achieve visual feedback and emotional expression based on semantics or speech.

[0022] This invention receives semantic signals or voice input, determines the facial geometry that can be driven, establishes a mapping relationship by calling a preset semantic-expression mapping rule library, and converts the input into control commands to drive the facial geometry, adjusting parameters to produce corresponding facial expressions. This technical solution enables the expression control model to generate lip movements, facial expressions, and head posture changes in real time based on semantic or voice signals, achieving visual feedback and emotional expression, thereby solving the problems of virtual intelligent agents lacking emotional expression capabilities and having a stiff interactive experience.

[0023] Furthermore, a speech recognition, understanding, and synthesis module is integrated and aligned with the facial expression control model to generate a multimodal interaction model, including: A voice interaction processing link consisting of speech recognition, semantic understanding and speech synthesis modules is constructed to realize closed-loop interaction of voice input recognition, semantic parsing and voice output; The voice interaction processing link is connected to the facial expression control model to establish a mapping relationship between the output of the voice interaction processing link and the facial expression driving parameters of the facial expression control model. This enables the speech recognition result to trigger the facial expression control model to generate corresponding facial expressions after semantic understanding, and / or the speech synthesis output to keep synchronized with the lip movements of the facial expression control model. The docked voice interaction processing link and facial expression control model are integrated into a unified multimodal interaction model, which enables the multimodal interaction model to have voice interaction capabilities and realize the linkage between voice and facial expressions.

[0024] This invention integrates speech recognition, semantic understanding, and speech synthesis modules into a speech interaction processing link. This link is then interfaced with an facial expression control model, establishing a mapping relationship between the speech processing link output and facial expression driving parameters. This allows speech recognition results to trigger facial expressions during speech interaction, and the speech synthesis output to synchronize with lip movements. This technical solution deeply integrates speech interaction capabilities with facial expression control capabilities, enabling the generated intelligent agent to simultaneously engage in speech communication with corresponding facial expressions, achieving expressive and human-like interaction. This solves the problem of the separation and difficulty in coordination between speech and facial expressions in multimodal interaction.

[0025] Furthermore, based on the joint motion parameters in the multimodal interaction model, virtual actions are mapped to entity driving units to generate an entity motion control structure, including: Based on the joint motion parameters in the multimodal interaction model, determine the range of motion, dependency level, and degree of freedom of each joint; Based on joint motion parameters, physical drive units are configured at the joints of the physical device corresponding to the multimodal interaction model; Based on the joint motion parameters and the physical characteristics of the physical drive unit, a parameter mapping relationship between virtual joint movements and physical drive signals is established. The parameter mapping relationship exists in the form of a mapping table or a conversion function, which is used to convert virtual motion commands into corresponding physical drive signals. The configured entity drive unit and the parameter mapping relationship are integrated into an entity motion control structure; the entity motion control structure is used to receive virtual motion commands, convert them into physical drive signals through the parameter mapping relationship, and send them to the corresponding entity drive unit to drive the entity joint movement.

[0026] This invention determines the range of motion, attachment level, and degrees of freedom of each joint based on joint motion parameters. Physical drive units are then positioned at the joint locations in the physical device. A parameter mapping relationship is established between virtual joint movements and physical drive signals based on the joint motion parameters and the physical characteristics of the drive units. The drive units and the mapping relationship are then integrated into a physical motion control structure. This technical solution enables precise matching between the joint structure in the virtual model and the mechanical axes of the actual hardware. Virtual motion commands are converted into physical drive signals through the parameter mapping relationship, driving the physical joints to complete basic movements in real space. This solves the problem of a lack of consistent control links between virtual actions and physical execution.

[0027] Furthermore, based on the aforementioned entity motion control structure, semantic reasoning and decomposition of user task instructions are performed to generate a sequence of physical actions and drive their execution, including: The task instructions input by the user are parsed and converted into a task semantic structure, which includes at least one of an intent unit, a target object, and a location description. Based on the task semantic structure, the task is decomposed into multiple sub-steps that are executed sequentially, generating a sequence of physical actions; each sub-step corresponds to a virtual joint action and its execution parameters. Based on the parameter mapping relationship established in the entity motion control structure, the virtual joint actions corresponding to each sub-step in the physical action sequence are converted into physical drive signals, and the physical drive signals are sent to the corresponding entity drive unit to drive the entity motion control structure to execute the physical action sequence in sequence.

[0028] This invention performs semantic parsing, intent recognition, and element extraction on natural language task instructions, converting them into a task semantic structure containing intent units, target objects, and location descriptions. Based on this structure, the task is decomposed into multiple sub-steps to generate a sequence of physical actions. Using established parameter mapping relationships, the virtual joint actions corresponding to each sub-step are converted into physical drive signals and sent to the physical drive unit for execution. This technical solution enables the intelligent agent to understand high-level task instructions (such as "grab the cup"), decompose them step by step into executable physical action sequences such as detecting the target, moving closer, grasping, and returning, and drive the physical joints to execute them accurately. This solves the problem of the lack of an effective conversion mechanism between language cognition and physical execution, giving the intelligent agent the ability to perform real physical tasks.

[0029] Based on the same concept, the present invention also provides an embodied intelligence generation system based on an N-grid progressive mechanism, comprising: The text parsing module is used to parse natural language descriptions and generate standardized text prompts. The image generation module is used to generate standardized two-dimensional images based on standardized text prompts. The 3D modeling module is used to reconstruct a standardized 3D model from a normalized 2D image. The dynamic structure module is used to define joint motion parameters of a standardized 3D model and generate a dynamic 3D model. The facial expression control module is used to establish a mapping between semantics or speech and facial geometric structure driving parameters based on a dynamic 3D model, and generate an facial expression control model. The voice interaction module integrates the speech recognition, understanding, and synthesis modules, and aligns with the facial expression control model to generate a multimodal interaction model. The motion control module is used to map virtual actions to entity driving units based on joint motion parameters in the multimodal interaction model, thereby generating entity motion control structures. The task reasoning module is used to perform semantic reasoning and decomposition of task instructions based on the entity motion control structure, generate physical action sequences and drive execution. The multimodal control center is used to perform unified data management and logical connection of various modules, forming an embodied intelligent agent with a closed loop of cognition, interaction, action, and operation. The outputs of each module are standardized and used as standardized inputs for subsequent modules, forming a progressive generation chain.

[0030] This invention employs a modular design, comprising a text parsing module, an image generation module, a 3D modeling module, a dynamic structure module, an expression control module, a voice interaction module, a motion control module, a task reasoning module, and a multimodal control center. This modular design encapsulates the core capabilities required by an embodied intelligent agent—language understanding, visual presentation, 3D modeling, dynamic structure, expression control, voice interaction, entity motion, and task reasoning—into independent functional units. Each module has clearly defined responsibilities and boundaries, maintaining functional cohesion while achieving organic integration of multimodal capabilities through inter-module collaboration. This solves the problem of fragmented and difficult-to-coordinate multimodal capabilities in existing technologies.

[0031] The system explicitly defines that "the output of each module is standardized and used as the standardized input for subsequent modules," establishing a unified data interface specification at the system level. Standardized text prompts output by the text parsing module can be directly parsed by the image generation module; standardized 2D images output by the image generation module possess a clear topological structure and can be directly used by the 3D modeling module; and so on, ensuring unified alignment of data formats, semantic expressions, and structural parameters among modules. This mechanism ensures the integrity and consistency of multimodal information during inter-module transmission, avoiding interruptions in the generation chain due to format incompatibility, missing information, or semantic ambiguity, enabling the system to continuously run and complete the entire process from natural language input to embodied agent generation.

[0032] A multimodal control center is introduced, communicating with each functional module to provide unified data management and logical integration, coordinating cross-modal logical linkages. This center enables the system to simultaneously perform physical grasping tasks (executed by the task inference and motion control modules) with real-time voice narration (executed by the voice interaction module) and focused facial expressions (executed by the expression control module), breaking down the fragmented state of single-modality operation. Through this central scheduling mechanism, the system elevates modules with single execution capabilities into a complete embodied intelligent agent with the ability to coordinate interaction and collaboration, achieving a closed-loop chain of "cognition-interaction-action-operation".

[0033] Through the sequential connection of nine modules and the unified scheduling of a multimodal control center, a complete progressive chain is constructed from natural language input to the generation of an embodied intelligent agent. The text parsing module completes language cognition; the image generation and 3D modeling modules realize visual presentation and structural construction; the dynamic structure module endows the model with mobility; the facial expression control and voice interaction modules endow it with anthropomorphic interaction capabilities; and the motion control and task reasoning modules endow the entity with movement and task execution capabilities. Finally, the multimodal control center integrates these into a complete embodied intelligent agent. This progressive architecture enables the system to start from abstract natural language descriptions and generate a complete intelligent agent layer by layer, possessing capabilities in language understanding, visual presentation, dynamic structure, facial expression interaction, voice communication, entity movement, and task execution, thus solving the problem of the lack of end-to-end capabilities from cognition to execution in existing technologies.

[0034] Modular design gives the system excellent scalability and configurability. Functional modules can work collaboratively as a complete workflow, or be deployed independently or replaced according to application requirements. For example, only the first five modules can be used to generate a 3D image from text, or the last four modules can be used as a robot control link to perform tasks. This architecture enables the system to adapt to diverse application scenarios such as embodied intelligence development, multimodal generation systems, and intelligent robots, and has broad application prospects.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves continuous construction and standardized connection of multimodal capabilities through an N-grid progressive mechanism, and fully covers the entire link from language cognition to physical execution. This enables the generated embodied intelligent agent to have multiple capabilities such as language understanding, visual presentation, dynamic structure, facial expression interaction, voice communication, physical movement and task execution. Moreover, the capabilities form an organic synergy, which solves the defects of modal fragmentation, link interruption and capability fragmentation in the prior art, and meets the integrated construction requirements from natural language description to embodied execution. Attached Figure Description

[0036] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the embodied intelligence generation method based on the N-grid progressive mechanism in an embodiment of the present invention; Figure 2 This is a structural block diagram of the embodied intelligent generation system based on the N-grid progressive mechanism in this embodiment of the invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0040] Example 1 Reference Figure 1 This embodiment provides an embodied intelligence generation method based on an N-grid progressive mechanism, which includes the following steps: S1: Perform semantic parsing, intent recognition, and element extraction on the input natural language description to generate standardized text prompts.

[0041] The system receives natural language descriptions input by the user and generates standardized text prompts (first grid) through semantic parsing, intent recognition, and element extraction. These prompts serve as input for the subsequent image generation step (second grid). Specifically, the system first performs semantic parsing on the input natural language descriptions, analyzing syntactic structure and semantic relationships; simultaneously, it performs intent recognition to determine the user's true intent (such as generating an embodied intelligent agent, specifying appearance features, or interactive behaviors); and extracts key information units through element extraction.

[0042] For example, if a user inputs "Generate a small blue robot that can smile and say hello": the system, through the above processing, parses the object attributes as "blue" and "small", the morphological information as "robot", the facial expression feature as "smiling", and the voice behavior as "can say hello". These elements are organized into structured, standardized text prompts, such as "small blue robot with smiling facial expression and voice interaction capabilities".

[0043] The standardized text prompts retain all the key information from the original input and output it in a standardized format, ensuring that subsequent image generation steps can accurately understand and execute the user's intent. Through this step, abstract natural language is transformed into standardized machine-parseable instructions, laying the semantic foundation for multimodal progressive generation.

[0044] S2: Generate a standardized 2D image optimized for 3D reconstruction based on standardized text prompts.

[0045] This step is the text-to-image step. It receives the standardized text prompts output from the first grid (S1) as input and generates a standardized 2D image optimized for 3D reconstruction, which serves as the input for subsequent 3D modeling steps.

[0046] Specifically, the system first receives standardized text prompts generated in step S1, such as "a small blue robot with smiling expressions and voice interaction capabilities." Using a multimodal alignment algorithm, the semantic information in the standardized text prompts is mapped to the image generation process, generating multiple 2D images corresponding to the text semantics based on normalization constraints. These constraints include specifications for color, pose, and structural contours, ensuring that the generated images not only highly correspond to the text semantics but also possess clear topological information, serving as effective input standards for subsequent 3D modeling steps.

[0047] After generating multiple candidate 2D images, the system selects the image with the clearest topological structure and the most favorable conditions for subsequent 3D reconstruction as the final output of this step—the normalized 2D image. This image retains all the key features of the original text prompt (such as blue, small, robot shape, smiling expression, etc.), and is normalized to ensure that its color distribution, pose angle, and structural contour meet the input requirements for 3D reconstruction.

[0048] Through this step, the abstract natural language description is transformed into a structured two-dimensional visual representation. This achieves the initial transformation from semantic cognition to visual presentation and lays an accurate foundation for the subsequent construction of a geometrically supported three-dimensional model. The normalized two-dimensional image, as the output of step S2, will be directly used in the image-to-3D step of step S3.

[0049] S3: Based on the normalized two-dimensional image, generate a standardized three-dimensional model with topological integrity using a three-dimensional reconstruction algorithm.

[0050] This step is the image-to-3D generation step. It receives the normalized 2D image output from the second grid (S2) as input and generates a normalized 3D model with topological integrity, which serves as the input for the subsequent structure dynamization step. Specifically, it includes the following processing steps: S3.1 Feature Analysis and Information Extraction The system first receives the normalized 2D image output from step S2. This image is not an ordinary picture, but an optimized normalized image with clear structural features, designed to provide an accurate visual basis for 3D reconstruction. The system performs feature analysis on the input normalized 2D image, extracting texture, pose, and depth information through a feature mapping layer. This key information forms the data foundation for subsequent 3D reconstruction.

[0051] S3.2 Spatial Reconstruction and Initial Model Generation Using 3D reconstruction algorithms (including depth reconstruction algorithms or latent space mapping algorithms), the extracted texture, pose, and depth information are transformed into 3D spatial data, and spatial reconstruction is performed to generate an initial 3D model. This initial 3D model contains basic geometric and topological structures, reflecting the shape contour and spatial relationships of the target object.

[0052] S3.3 Model Standardization Processing To ensure that the generated 3D model can support subsequent structural dynamics and motion control, the system performs standardization processing on the generated initial 3D model. Standardization processing includes: Parameter standardization: The model's proportions, topology, and spatial parameters are standardized to ensure the model conforms to preset specifications; Information recording: The model's proportions, structural hierarchy, and surface texture information are recorded to provide geometric support for subsequent voice interaction and motion-driven processes. Through standardization, the initial 3D model is transformed into a standardized 3D model with topological integrity.

[0053] S3.4 Output standardized 3D model The final standardized 3D model output has the following characteristics: complete topological structure, easy joint definition, and the ability to meet the requirements of the next step (fourth grid) to embed motion parameters at joints, connection points, or key structures. This model not only accurately reflects the appearance of the target object, but also lays the structural foundation for the subsequent evolution from static geometry to dynamic embodiment through standardization.

[0054] The core of this step lies not only in completing the 2D to 3D transformation through a 3D reconstruction algorithm, but also in ensuring that the reconstructed model possesses the ability to evolve from "static geometry" to "dynamic embodiment" by standardizing its topological structure and spatial parameters. The standardized 3D model, as the output of step S3, will be directly used in the structure dynamization step of step S4.

[0055] S4: Define joint motion parameters for the standardized 3D model to generate a dynamic 3D model with a description of motion degrees of freedom.

[0056] This step is the structural dynamization step. It receives the standardized 3D model output from the third grid (S3) as input, defines joint motion parameters for the model, and generates a dynamic 3D model with motion degrees of freedom. This model serves as input for the subsequent expression control and entity motion capability generation stages. Specifically, it includes the following processing steps: S4.1 Structural Analysis and Region Identification The system first receives the standardized 3D model output from step S3, performs in-depth structural analysis, and identifies the hierarchical relationships and movable node regions of the model. Hierarchical relationships include the attachment relationships between components, such as an arm attaching to the torso. Movable node regions refer to key parts of the model that require the introduction of dynamic parameters and constraint logic, including at least one of limb connections, joints, rotation axes, and sliding connection points. Specifically, limb connections are areas that connect different limb components, joints are joint structures in the model responsible for movement, rotation axes are the axial positions that support the rotational movement of components, and sliding connection points are connection positions that support the sliding of components in a specific direction. Through this step, the system clarifies which parts of the model possess motion potential and the subordinate relationships between components.

[0057] S4.2, Define motion nodes Within the data structure corresponding to the identified movable node regions, the system defines motion nodes. Specifically, static geometric mesh points are marked as controllable logical nodes, transforming the model from a simple "shape" into a structure with "component" definitions. Each motion node corresponds to a potential controllable unit, laying the foundation for subsequent embedding of motion parameters.

[0058] S4.3 Embedded Motion Constraints and Definition of Joint Motion Parameters On the defined motion nodes, the system further embeds motion constraints and defines joint motion parameters to generate a parametric 3D model. Motion constraints include at least one of rotation angle, translation range, and physical constraints, used to limit the motion limits of the nodes (e.g., maximum arm rotation angle, slide rail travel range). Joint motion parameters include adjustable control variables and degrees of freedom parameters, used to specify which dimensions are movable (e.g., rotation around the X-axis, translation along the Y-axis). Through this step, each motion node is given specific physical meaning and control attributes, and the model possesses the basic conditions for being driven.

[0059] S4.4 Introduce the skeleton control chain and establish mapping relationships In the topology of the parametric 3D model, the system introduces a skeleton control chain to establish a mapping relationship from joint motion parameters to geometric deformation. The specific implementation includes the following sub-steps: Constructing the Skeletal System: Based on the motion nodes defined in the parametric 3D model, a skeletal system corresponding to the model's geometry is constructed. The skeletal system contains multiple skeletal nodes, each corresponding to a movable part of the model and associated with a motion node. The skeletal node is the geometric representation of the motion node, used to drive the corresponding geometric part.

[0060] Organize the parent-child hierarchical structure: Based on the hierarchical relationship identified in step S4.1, organize multiple skeletal nodes into a tree-like parent-child hierarchical structure. In this structure, the movement of the parent node is transmitted to the child node. For example, the movement of the upper arm skeletal node (parent) will cause the forearm skeletal node (child) and the hand skeletal node (grandchild) to produce corresponding movements.

[0061] Binding the geometry mesh and assigning weights: The geometry mesh of the parametric 3D model is bound to the skeletal system, and weights are assigned to each vertex on the geometry mesh based on its influence from each bone node. The weights determine the influence of each bone node on the geometry vertex. For example, a vertex near the elbow joint may be influenced by both the upper arm bone and the forearm bone, each with a weight of 0.5.

[0062] Associating Joint Motion Parameters with Mapping Rules: Associating the joint motion parameters defined in step S4.3 with the corresponding bone nodes, and setting the mapping rules between the joint motion parameters and the bone node movements. These rules define how the corresponding bone nodes should move when a joint motion parameter (such as "shoulder joint rotation 30 degrees") is input.

[0063] Calculating geometric deformation: When joint motion parameters change, the system calculates the position and orientation of the affected bone nodes based on the parent-child hierarchical structure, and recalculates the position of each vertex on the geometric mesh using pre-assigned weights to generate the corresponding geometric deformation. For example, when the "raise hand" parameter is input, the shoulder joint bone nodes rotate, causing the upper arm, forearm, and hand bone nodes to move. This, in turn, drives the geometric mesh to produce the correct hand-raising motion through weight calculation, rather than producing broken surfaces or incorrect stretching.

[0064] By constructing the aforementioned skeleton control chain, the system establishes a precise mapping relationship from joint motion parameters to geometric deformation, ensuring that changes in driving parameters can trigger correct geometric linkages in the model.

[0065] S4.5 Output dynamic 3D model After the above processing, the system outputs a dynamic 3D model. This dynamic 3D model includes joint motion parameters, hierarchical dependencies, and physical constraints, and can directly receive subsequent control signals to achieve posture changes and motion execution. This model achieves structural-level mobility at the 3D geometric level, giving the model itself the attribute of being "driveable," rather than merely remaining at the virtual simulation level.

[0066] The core of this step lies in: identifying movable regions through structural analysis, endowing the model with controllable attributes through motion node definition and parameter embedding, establishing a mapping relationship from parameters to deformation through a skeleton control chain, and ultimately transforming the static, standardized 3D model into a dynamic 3D model with realistic geometric mobility. This lays the structural foundation for subsequent expression control, voice interaction, and entity motion capability generation. The dynamic 3D model, as the output of step S4, will be directly used in the expression control step of step S5 and the entity motion capability generation step of step S7.

[0067] S5: Based on the dynamic 3D model, establish a mapping relationship between semantic or speech input and facial geometric structure driving parameters, and generate an expression control model that can be driven by semantics or speech based on this mapping relationship.

[0068] This step involves visual interaction and facial expression control. Based on the dynamic 3D model output from the fourth grid (S4), a mapping relationship is established between semantic or speech input and facial geometric structure driving parameters. Based on this mapping relationship, a semantically or speech-driven facial expression control model is generated as input for the subsequent speech interaction integration step. Specifically, the following processing steps are included: S5.1 Receive multimodal input signals The system first receives semantic signals or voice input. Semantic signals can originate from text commands directly entered by the user or structured semantic units after semantic parsing; voice input consists of voice commands acquired in real time. These inputs constitute the raw signals that drive changes in facial expressions.

[0069] S5.2 Determine the actuable facial geometry Based on the dynamic 3D model output in step S4, the system determines the actuated facial geometry within the model. The facial geometry includes at least one of the following: eye structure, mouth structure, and head structure. Specifically, the eye structure corresponds to independently actuated geometric components such as the eyeball and eyelids; the mouth structure corresponds to mouth-shaped components such as the lips and jaw; and the head structure corresponds to the overall head posture (e.g., nodding or shaking). Since the dynamic 3D model has already been defined in step S4 with actuated underlying parameters and structural constraints through joint motion parameters, these facial geometry structures have pre-defined actuated parameter interfaces.

[0070] S5.3, Call the semantic-expression mapping rule base to establish mapping relationships. The system invokes a pre-defined semantic-expression mapping rule library to map and bind the received semantic signals or speech input to the driving parameters of facial geometry, establishing a mapping relationship between the semantic or speech input and the driving parameters. This rule library pre-stores the correspondence between abstract emotions or intentions (such as "happy" or "surprised") and specific facial action parameters (such as the angle of the corners of the mouth and the degree of eyelid opening and closing), as well as synchronization rules between speech features (such as pitch and speech rate) and lip shape parameters. Through this step, the input semantics or speech is transformed into parameterized instructions that can be executed by the facial geometry.

[0071] S5.4, converts into control commands and drives facial geometry. Based on the mapping relationship established in step S5.3, the system converts semantic signals or voice input into specific control commands and drives facial geometry. For example, when the input is the semantic "smile," the system generates control commands according to the mapping rules, driving the eye structure (such as squinting) and mouth structure (raising the corners of the mouth) to move in coordination; when the input is the voice "hello," the system synchronously drives the mouth structure to produce corresponding mouth shape changes according to the voice content and prosodic features, and may coordinate with head posture (such as nodding).

[0072] S5.5 Adjust parameters to generate facial expressions and output the facial expression control model. Based on control commands, the system adjusts the corresponding parameters of the facial geometry to generate corresponding facial expressions in three-dimensional geometry. These expressions include at least one of lip movements, facial expressions, and head posture changes. For example, lip movements can represent lip-syncing to spoken content, facial expressions can represent emotions such as smiling, blinking, and surprise, and head posture changes can represent actions such as nodding, shaking, and tilting. By adjusting geometric parameters in real time, the model visually presents coherent and natural facial expressions, thus completing the expression rendering process.

[0073] Finally, the system outputs an expression control model. This model is built on a dynamic 3D model and embeds the mapping relationship between semantic or speech input and facial geometric structure driving parameters. It can trigger corresponding facial expressions in real time based on subsequent semantic or speech signals, realizing visual feedback and emotional expression.

[0074] The core of this step lies in: relying on the dynamic 3D model generated in step S4, transforming abstract semantic or speech input into specific facial geometric driving parameters through a semantic-expression mapping rule base, enabling the model to automatically adjust expressions based on the interactive content, thereby enhancing the agent's emotional expression and visual interactivity. The expression control model, as the output of step S5, will be directly used in the voice interaction integration step of step S6.

[0075] S6: Integrates a speech recognition module, a semantic understanding module, and a speech synthesis module, and aligns them with an facial expression control model to generate a multimodal interaction model with speech-facial expression linkage capabilities.

[0076] This step is the voice interaction integration step. Based on the facial expression control model output from the fifth grid (S5), it integrates the speech recognition module, semantic understanding module, and speech synthesis module, and aligns them with the facial expression control model to generate a multimodal interaction model with voice-facial expression linkage capabilities. This model serves as the input for the subsequent entity motion capability generation step. Specifically, it includes the following processing steps: S6.1, Constructing a Voice Interaction Processing Link First, the speech recognition module (ASR), semantic understanding module (NLU), and speech synthesis module (TTS) are integrated to construct a voice interaction processing loop. This loop is used to realize a closed-loop interaction of voice input recognition, semantic parsing, and voice output. Specifically, the speech recognition module converts the user's voice commands into text; the semantic understanding module performs intent recognition and semantic parsing on the text to determine the user's interaction intent and response content; and the speech synthesis module synthesizes the response content into voice output, thus forming a complete voice interaction loop. For example, when a user issues the voice command "Hello," the system recognizes it as the text "Hello" through ASR, parses it as a greeting intent through NLU, and generates the response voice "Hello, I'm here" through TTS, which is then output as synthesized speech.

[0077] S6.2 Establish a mapping relationship with the facial expression control model. The constructed voice interaction processing link is then integrated with the facial expression control model generated by S5 to establish a mapping relationship between the output of the voice interaction processing link and the facial expression driving parameters of the facial expression control model. This mapping relationship enables the facial expression control model to generate corresponding facial expressions after semantic understanding of the speech recognition results during voice interaction, while the speech output from the speech synthesis remains synchronized with the lip movements of the facial expression control model. Specifically: When NLU parses out a specific semantic (such as "happy" or "surprised"), it generates corresponding expression-driving parameters based on the semantic-expression mapping rules established in S5, which in turn drive the expression control model to produce corresponding facial expression changes (such as smiling or surprised). When TTS synthesized speech is output, the system calculates the corresponding lip shape parameters in real time based on the speech duration, phoneme sequence and other information, and drives the mouth structure in the expression control model to produce synchronous lip shape changes, so as to achieve accurate matching between speech and lip shape. During voice interaction, head posture changes (such as nodding) can be driven based on the prosodic features of the voice (such as tone and stress) to enhance the naturalness of the interaction.

[0078] Through this connection, the dynamic capabilities (visual feedback) of the "face" constructed in the fifth grid are organically combined with the language capabilities (voice interaction) of the "brain" and "mouth" constructed in the sixth grid, laying the foundation for the subsequent realization of "expressive" anthropomorphic interaction.

[0079] S6.3, Integrate and generate a multimodal interaction model Finally, the integrated voice interaction processing chain and facial expression control model are combined into a unified multimodal interaction model. This model not only possesses independent voice interaction capabilities (understanding, thinking, and responding), but also can drive facial expressions in real time based on voice content and semantic features during voice interaction, achieving coordinated interaction between voice and facial expressions. For example, during the execution of a task, the agent can simultaneously provide voice explanations and use focused facial expressions, thus presenting a human-like interaction effect.

[0080] The core of this step lies in: based on the existing facial expression control model, by integrating a voice module and establishing a mapping relationship with facial expression driving parameters, the voice capability is superimposed onto the visual model, forming a multimodal interaction model with voice-facial expression linkage capability. This multimodal interaction model, as the output of step S6, will be directly used in the entity motion capability generation step of step S7.

[0081] S7: Based on the joint motion parameters contained in the multimodal interaction model, virtual actions are mapped to physical driving units to generate physical motion control structures; the physical motion control structures are used to convert virtual action commands into physical driving signals to drive physical joint movements and realize basic actions.

[0082] This step is the entity motion capability generation step. Based on the multimodal interaction model output by the sixth grid (S6)—which contains joint motion parameters derived from step S4—virtual actions are mapped to entity driving units to generate entity motion control structures. These structures convert virtual action commands into physical driving signals to drive entity joint movement and achieve basic actions. Specifically, the following processing steps are included: S7.1 Determine the joint motion properties First, based on the joint motion parameters contained in the multimodal interaction model output by S6, the system determines the range of motion, attachment level, and degrees of freedom of each joint. The joint motion parameters originate from the joint motion parameter definitions made on the standardized 3D model in step S4 and are retained in the multimodal interaction model of step S6. By parsing these parameters, the system clarifies the range of motion of each joint (such as rotational angle limits and translational stroke), its attachment relationship in the kinetic chain (such as an arm attaching to the torso), and the dimensions of its degrees of freedom (such as rotation around the X-axis and translation along the Y-axis), laying the foundation for subsequent physical mapping.

[0083] S7.2, Configure the physical drive unit Based on the aforementioned joint motion parameters, the system configures physical drive units at the joint positions of the physical device corresponding to the multimodal interaction model (i.e., the physical entity obtained after materializing the virtual model through 3D printing or other manufacturing methods). Each joint position defined in the virtual model (such as limb connections, rotation nodes, and moving components) corresponds to a specific mechanical position in the physical device, and the system installs corresponding physical drive units at these positions. The physical drive unit includes at least one of a servo motor, a stepper motor, or a drive wheel. Through this structural correspondence, the joint structure in the virtual model is precisely matched with the mechanical axes of the actual hardware.

[0084] S7.3 Establish parameter mapping relationship Based on the joint motion parameters and the physical characteristics of the physical drive unit, the system establishes a parameter mapping relationship between virtual joint movements and physical drive signals. This parameter mapping relationship exists in the form of a mapping table or transformation function, used to convert the virtual joint movements contained in subsequently generated motion commands into corresponding physical drive signals.

[0085] For example, for a servo-driven rotary joint, the mapping relationship can be defined as "virtual joint rotation angle" corresponding to "servo PWM signal duty cycle"; for a wheeled movement mechanism, the mapping relationship can be defined as "virtual movement speed" corresponding to "motor drive voltage". This mapping relationship fully considers the matching between joint motion parameters (such as range of motion, degrees of freedom) and the physical characteristics of the drive unit (such as servo rotation angle range, motor response curve), ensuring that virtual motion commands can be accurately converted into physical signals that can be executed by the entity.

[0086] S7.4, integrated into a solid motion control structure The configured physical drive units and parameter mapping relationships are integrated into a physical motion control structure. This structure comprises two core components: first, physical drive units installed at the joints of the physical device; and second, mapping logic that converts virtual motion commands into physical drive signals. Together, they form a complete control unit, establishing a one-to-one drive relationship between the virtual model and the physical device.

[0087] S7.5, Output Entity Motion Control Structure Finally, the system outputs a physical motion control structure. This structure has the following functions: it can receive virtual motion commands (such as "rotate the shoulder joint 30 degrees" or "move the chassis forward 1 meter"), convert the virtual joint movements contained in the commands into corresponding physical drive signals (such as PWM signals, serial port signals, or CAN bus signals) through the internally established parameter mapping relationship, and send the signals to the corresponding physical drive unit to drive the physical joint to complete the movements in the real space according to the set trajectory, realizing basic movements such as raising the hand, turning the head, and moving.

[0088] During motion execution, the system can simultaneously connect to attitude sensors, encoders, or other feedback components to collect the actual motion state of the physical joints and make real-time corrections to the physical drive signals based on the feedback information, so as to achieve stable control and error correction of the motion.

[0089] The core of this step lies in establishing a parameter mapping relationship between virtual joint movements and physical drive signals. This maps the virtual joint motion capabilities defined in step S4 to the physical drive unit, enabling the model to evolve from a virtual movable structure into an embodied unit with physical motion capabilities, thus completing the progression from digital motion potential to real-world motion execution. The physical motion control structure, as the output of step S7, will be directly used in the task reasoning and physical execution steps of step S8.

[0090] S8: Based on the entity motion control structure, perform semantic reasoning and task decomposition on the user-input task instructions, generate an executable physical action sequence, and drive the entity motion control structure to execute the physical action sequence.

[0091] This step involves task reasoning and entity execution. Based on the entity motion control structure output from the seventh grid (S7), semantic reasoning and task decomposition are performed on the user-input task instructions to generate an executable sequence of physical actions, which is then driven by the entity motion control structure to execute the sequence. Specifically, this includes the following processing steps: S8.1 Parsing task instructions to generate task semantic structure First, the system receives a natural language task instruction input by the user, such as "Please pick up the cup on the table." This instruction undergoes semantic parsing, intent recognition, and element extraction, transforming it into a task semantic structure. The task semantic structure includes at least one of an intent unit, a target object, and a location description. Taking the example instruction, the system parses the following: the intent unit is "grab," the target object is "cup," and the location description is "on the table." Through this step, the abstract natural language instruction is transformed into a structured task semantic representation, clarifying core elements such as "what to do," "to whom to do it," and "where to do it."

[0092] S8.2 Decompose the task to generate a sequence of physical actions Based on the above task semantic structure, the system decomposes the task into multiple sequentially executed sub-steps, generating an executable sequence of physical actions. Each sub-step in the physical action sequence corresponds to a virtual joint action and its execution parameters. The execution parameters include at least one of joint angle change, movement path planning, and end effector opening / closing amplitude. Taking the "grabbing a cup" task as an example, the system can decompose it into the following sub-steps: Sub-step 1: Target detection – Identify and locate the cup on the table using a visual sensor, and obtain the target's coordinates; Sub-step 2: Move to the target location – Plan the movement path and drive the chassis or leg joints to move near the cup; Sub-step 3: Extend the robotic arm – Calculate the target angles of each joint of the robotic arm based on the target position, and prepare to grasp; Sub-step 4: Perform gripping – Control the end effector (such as a gripper) to close with an appropriate opening and closing range to grasp the cup; Sub-step 5: Lift the target – Drive the robotic arm joints to lift the cup to the specified height.

[0093] Each sub-step corresponds to a specific virtual joint action (such as "shoulder joint rotates 30 degrees" or "gripper closes 20 mm") and its execution parameters (such as rotation speed, closing force, and path point sequence), providing a precise motion description for subsequent physical execution.

[0094] S8.3, Converting parameters into physical drive signals using parameter mapping relationships. Based on the parameter mapping relationships established in the entity motion control structure generated in step S7, the system converts the virtual joint actions corresponding to each sub-step in the physical action sequence into physical drive signals. The parameter mapping relationships exist in the form of mapping tables or transformation functions, pre-defining the correspondence between virtual joint actions (such as "shoulder joint rotation 30 degrees") and physical drive signals (such as the duty cycle of a servo PWM signal). Through this step, the motion description in virtual space is transformed into physical instructions executable by the entity drive unit.

[0095] S8.4, Sending signals to drive the entity motion control structure to execute The converted physical drive signals are sent to the corresponding physical drive units, driving the physical motion control structure to execute the sequence of physical actions sequentially. For example, first, a command is sent to the vision sensor module to perform target detection; after the target is detected, a movement command is sent to the chassis drive unit; after reaching the target position, drive signals for each joint of the robotic arm are sent sequentially; finally, gripper drive signals are sent to complete the grasping. By sending signals in sequence, the system ensures that each sub-step is executed in an orderly manner, ultimately completing the overall task.

[0096] S8.5 Real-time correction during execution During the execution of physical action sequences by the driving entity motion control structure, the system can also make real-time corrections to the execution process based on at least one of visual feedback, force feedback, or pose feedback. For example, during grasping, if the force feedback sensor detects abnormal contact force between the gripper and the cup, the system can adjust the gripper opening and closing range or grasping force in real time; if visual feedback detects movement of the target position, the system can dynamically adjust the movement trajectory of the robotic arm. Through feedback correction, the accuracy and reliability of task actions are ensured, enabling the intelligent agent to adapt to environmental changes and complete embodied tasks such as grasping, handling, and delivery.

[0097] The core of this step lies in: transforming high-level natural language task instructions into structured task semantics through semantic parsing, then decomposing them into executable physical action sequences, and using the parameter mapping relationship established in step S7 to convert virtual actions into physical driving signals, driving the entity motion control structure to execute, thereby achieving a seamless transformation from language cognition to physical execution. The execution process of the physical action sequence is completed by the entity motion control structure, enabling the intelligent agent to possess the ability to perform real physical tasks.

[0098] S9: Integrates standardized text prompts, standardized 2D images, standardized 3D models, dynamic 3D models, facial expression control models, multimodal interaction models, entity motion control structures, and physical action sequences. Through the multimodal control center, it realizes data interaction and logical coordination between each step to form a complete embodied intelligent agent.

[0099] This step is the embodied intelligent agent generation step. It integrates the standardized text prompts, normalized 2D images, standardized 3D models, dynamic 3D models, facial expression control models, multimodal interaction models, entity motion control structures, and physical action sequences generated in the previous steps. Data interaction and logical coordination between the steps are achieved through a multimodal control center, forming a complete embodied intelligent agent. Specifically, it includes the following processing steps: S9.1 Integrating the results of each step The system first compiles the core deliverables generated in steps S1 to S8, specifically including: standardized text prompts generated in S1, demonstrating language cognition and parsing capabilities; standardized two-dimensional images generated in S2, demonstrating visual presentation capabilities; standardized three-dimensional models generated in S3, demonstrating three-dimensional structure construction capabilities; dynamic three-dimensional models generated in S4, demonstrating joint dynamism and structural mobility; facial expression control models generated in S5, demonstrating facial feedback and visual interaction capabilities; multimodal interaction models generated in S6, demonstrating voice interaction and facial expression linkage capabilities; entity motion control structures generated in S7, demonstrating entity motion control capabilities; and physical action sequences generated in S8, demonstrating task reasoning and execution capabilities.

[0100] These achievements cover a complete capability chain from language cognition, visual presentation, 3D modeling, dynamic structure, facial expression interaction, voice communication to physical movement and task execution, and are the core elements constituting embodied intelligent agents.

[0101] S9.2, Data management and logical connection are achieved through a multimodal control center. The system introduces a multimodal control center, which connects with the capability results generated in the above steps to achieve unified data management and logical integration. As the central scheduling mechanism, the multimodal control center is responsible for: Data management: Unified storage, indexing, and retrieval of data generated in each step to ensure that data from different modalities can be accessed efficiently and used collaboratively; Logical connection: Establish logical connections between various capability modules and define triggering conditions and response rules for cross-modal interactions; Task coordination: Dynamically schedule the collaborative work of various capability modules according to the current task requirements to ensure the orderly conduct of multimodal interactions.

[0102] For example, the multimodal control center logically associates the facial expression control model generated by S5 with the multimodal interaction model generated by S6, enabling real-time triggering of facial expression changes during voice interaction; it maps and associates the entity motion control structure generated by S7 with the physical action sequence generated by S8, enabling task instructions to drive entity motion; and it unifies and coordinates the capabilities of S5, S6, S7, and S8, enabling the agent to perform physical grasping tasks while coordinating real-time voice explanations and focused facial expressions.

[0103] S9.3, Implement cross-modal logic linkage Through unified scheduling by the multimodal control center, the system achieves cross-modal logical linkage between various capability modules, breaking the fragmented state of single-modality operation. Specifically, this manifests as follows: Voice-expression linkage: When the intelligent agent interacts with the S6 via voice, the multimodal control center drives the facial geometry in real time through the S5 expression control model based on the voice content or semantic features, generating corresponding mouth shape changes, facial expressions and head posture, so that voice and expression are synchronized and coordinated. Action-voice linkage: When the agent performs a physical task through S8, the multimodal control center can trigger the voice output of S6 according to the task execution status, such as issuing a voice explanation of "I am grabbing a cup" while performing a grasping action; Action-expression linkage: During task execution, the multimodal control center can trigger the S5 expression control model based on the task context (such as successful grabbing or encountering obstacles) to generate corresponding emotional expressions (such as smiling, focusing, or puzzled). Multimodal collaboration: In complex task scenarios, the multimodal control center coordinates the simultaneous operation of multiple capabilities such as language understanding, visual perception, voice interaction, facial expression feedback, and physical movement, enabling the intelligent agent to exhibit a human-like comprehensive performance.

[0104] S9.4 Formation of a complete embodied intelligent entity Through the above integration and collaboration, the system ultimately forms a complete embodied intelligent agent. This agent possesses the following comprehensive capabilities: Cognitive ability: Able to understand natural language instructions and interpret task intent (derived from S1 and S8); Visual ability: Possesses visual presentation and three-dimensional structure cognition ability (derived from S2 and S3); Dynamic structural capability: Possesses movable joint structure and posture control capability (derived from S4); Facial expression interaction capability: It can generate corresponding facial expressions based on semantics or speech to express emotions (derived from S5); Voice interaction capability: It can perform speech recognition, semantic understanding and speech synthesis to achieve natural language communication (derived from S6). Physical motion capability: Able to drive physical joints to complete basic movements (derived from S7); Task execution capability: It can decompose high-level task instructions into executable sequences of physical actions and drive entities to execute (derived from S8). Multimodal collaboration capability: It can coordinate the above-mentioned capabilities to achieve synchronous linkage of voice, facial expressions and actions during task execution (derived from S9).

[0105] This intelligent agent begins with a natural language description and progresses through text parsing, image generation, 3D modeling, structure dynamization, facial expression control, voice interaction, entity motion control, and task reasoning, ultimately generating a complete embodied intelligent agent capable of performing tasks in a physical environment. It can not only understand language, express emotions, and communicate via voice, but also control its own limb structure to complete practical tasks in real physical space, including moving, grasping, and manipulating objects.

[0106] The core of this step lies in: unifying and logically connecting the independent capabilities generated in the aforementioned steps through a multimodal control center, realizing cross-modal collaborative linkage, and upgrading the "machine entity" with single execution capabilities into a complete "embodied intelligent agent" with overall interaction and collaboration capabilities. This achieves a closed-loop chain of "cognition-interaction-action-operation" and provides a unified progressive solution for the construction of embodied artificial intelligence systems.

[0107] The method framework of this invention not only supports a full-link generation mode but also allows some grids to operate independently. For example, only the first five grids (S1 to S5) can be used to complete the generation process from text to a 3D image, or the last five grids (S5 to S9) can be used as a robot control link to realize entity execution tasks. Therefore, this method has high configurability and customizability, and the generation path can be tailored or internal modules can be replaced according to actual application needs, thereby realizing different types of intelligent agent construction tasks.

[0108] Through the above design, this invention enables a continuous and progressive process from natural language input to the generation of embodied intelligent agents, overcoming the technical problem of lack of structured connections between modalities in traditional multimodal generation systems, and achieving unified representation, logical coherence, and dynamic collaboration of cross-modal information. Because this invention is based on a progressive N-grid mechanism, each stage can evolve independently or be combined and expanded as needed, thus possessing high portability, scalability, and continuous evolution potential.

[0109] This invention constructs an N-grid progressive generation framework consisting of modules such as language understanding, visual generation, voice interaction, structural dynamics, entity motion control, and task reasoning. It realizes a complete embodied intelligence link from cognitive generation to entity behavior execution, providing a systematic, scalable, and practical technical foundation for the development of embodied artificial intelligence systems.

[0110] Example 2 This embodiment provides an embodied intelligence generation system based on an N-grid progressive mechanism, used to implement the embodied intelligence generation method in Embodiment 1. For example... Figure 2 As shown, the system includes the following modules: The text parsing module performs semantic analysis, intent recognition, and element extraction on the input natural language description, generating standardized text prompts. Specifically, when a user inputs a natural language description (e.g., "Generate a small blue robot that can smile and say hello"), the text parsing module analyzes the sentence structure using natural language processing techniques, identifies the user's intent (e.g., generate an embodied agent), and extracts key elements: object attributes (blue, small), morphological information (robot), facial features (smiling), and vocal behavior (can say hello). These elements are organized into structured standardized text prompts, such as "small blue robot, smiling, can interact via voice," which serve as input for the subsequent image generation module.

[0111] The image generation module generates standardized 2D images optimized for 3D reconstruction based on standardized text prompts. Specifically, the module receives standardized text prompts from the text parsing module, uses a multimodal alignment algorithm to map the semantic information in the prompts to the image generation process, and generates multiple 2D images corresponding to the text semantics based on normalization constraints. These constraints include standardization of color, pose, and structural contours, ensuring that the generated images possess clear topological information and can serve as effective input standards for subsequent 3D modeling stages. The image generation module selects the image with the clearest topological structure and best suited for subsequent 3D reconstruction from the generated 2D images as its final output—the standardized 2D image.

[0112] The 3D modeling module is used to generate a standardized 3D model with topological integrity from a normalized 2D image using a 3D reconstruction algorithm. Specifically, the 3D modeling module receives the normalized 2D image output from the image generation module, performs feature analysis on the image, and extracts texture, pose, and depth information. The extracted information is then converted into 3D spatial data using a 3D reconstruction algorithm, and spatial reconstruction is performed to generate an initial 3D model. Subsequently, the initial 3D model undergoes standardization processing, including unifying the model's proportions, topology, and spatial parameters, and recording the model's shape proportions, structural hierarchy, and surface texture information. Finally, a standardized 3D model with topological integrity and easy joint definition is output.

[0113] The dynamic structure module defines joint motion parameters for a standardized 3D model, generating a dynamic 3D model with degrees of freedom. Specifically, the dynamic structure module receives the standardized 3D model output from the 3D modeling module, performs structural analysis, and identifies the hierarchical relationships and movable node regions. Motion nodes are defined in the data structures corresponding to the identified movable node regions, and motion constraints are embedded in these nodes along with joint motion parameters, generating a parametric 3D model. Furthermore, a skeleton control chain is introduced into the topology of the parametric 3D model to establish a mapping relationship from joint motion parameters to geometric deformation, generating the dynamic 3D model. This dynamic 3D model includes joint motion parameters, hierarchical dependencies, and physical constraints, enabling it to directly receive subsequent control signals to achieve posture changes and motion execution.

[0114] The expression control module establishes a mapping relationship between semantic or speech input and facial geometric structure driving parameters based on a dynamic 3D model, and generates an expression control model that can be driven by semantics or speech based on this mapping relationship. Specifically, the expression control module receives semantic signals or speech input, determines the facial geometric structures that can be driven based on the dynamic 3D model output by the dynamic structure module, calls a preset semantic-expression mapping rule library, and maps and binds the received semantic signals or speech input with the driving parameters of the facial geometric structures to establish a mapping relationship between semantic or speech input and driving parameters. According to this mapping relationship, the semantic signals or speech input are converted into control commands to drive the facial geometric structures, adjust the parameters of the facial geometric structures, generate corresponding facial expressions in 3D geometry, and output the expression control model.

[0115] The voice interaction module integrates the speech recognition, semantic understanding, and speech synthesis modules, aligning them with the facial expression control model to generate a multimodal interaction model with voice-expression linkage capabilities. Specifically, the voice interaction module first integrates the speech recognition, semantic understanding, and speech synthesis modules into a voice interaction processing link, which enables closed-loop interaction of voice input recognition, semantic parsing, and voice output. Then, it interfaces with the facial expression control model output by the facial expression control module, establishing a mapping relationship between the output of the voice interaction processing link and the facial expression driving parameters of the facial expression control model. This ensures that during voice interaction, the speech recognition result, after semantic understanding, can trigger the facial expression control model to produce corresponding facial expressions, and / or the speech output from the speech synthesis module remains synchronized with the lip movements of the facial expression control model. Finally, the interfaced voice interaction processing link and facial expression control model are integrated into a unified multimodal interaction model output.

[0116] The motion control module maps virtual actions to physical drive units based on the joint motion parameters contained in the multimodal interaction model, generating a physical motion control structure. Specifically, the motion control module receives the multimodal interaction model output by the voice interaction module and determines the range of motion, dependency level, and degrees of freedom of each joint based on the joint motion parameters contained in the model. Based on the joint motion parameters, physical drive units are configured at the joint positions of the physical device corresponding to the multimodal interaction model. According to the joint motion parameters and the physical characteristics of the physical drive units, a parameter mapping relationship is established between virtual joint actions and physical drive signals. This parameter mapping relationship exists in the form of a mapping table or transformation function, used to convert the virtual joint actions contained in the virtual action commands into corresponding physical drive signals. The configured physical drive units and the parameter mapping relationship are integrated into a physical motion control structure and output. This physical motion control structure can receive virtual action commands, convert them into physical drive signals through the parameter mapping relationship, and send them to the corresponding physical drive units to drive the physical joints to achieve basic actions.

[0117] The task reasoning module performs semantic reasoning and task decomposition on user-inputted task instructions based on the entity motion control structure, generating an executable sequence of physical actions and driving the entity motion control structure to execute the sequence. Specifically, the task reasoning module receives natural language task instructions input by the user (e.g., "Please pick up the cup on the table"), performs semantic parsing, intent recognition, and element extraction on the instructions, converting them into a task semantic structure (including at least one of intent units, target objects, and location descriptions). Based on the task semantic structure, the task is decomposed into multiple sequentially executed sub-steps, generating an executable sequence of physical actions. Each sub-step in the physical action sequence corresponds to a virtual joint action and its execution parameters (including at least one of joint angle changes, movement path planning, and end effector opening / closing amplitude). Based on the established parameter mapping relationship in the entity motion control structure output by the motion control module, the virtual joint actions corresponding to each sub-step in the physical action sequence are converted into physical drive signals, and these signals are sent to the corresponding entity drive units to drive the entity motion control structure to execute the physical action sequence sequentially.

[0118] The multimodal control center communicates with the text parsing module, image generation module, 3D modeling module, dynamic structure module, facial expression control module, voice interaction module, motion control module, and task reasoning module. It manages data and logically connects these modules, coordinating cross-modal logical linkages to form a complete embodied intelligent agent with a closed-loop capability encompassing cognition, interaction, action, and operation. Specifically, the multimodal control center is responsible for: The data output from each module is stored, indexed, and retrieved in a unified manner to ensure that data from different modalities can be accessed efficiently and used collaboratively. Establish logical connections between modules and define triggering conditions and response rules for cross-modal interactions; Based on current task requirements, dynamically schedule the collaborative work of each module to ensure the orderly conduct of multimodal interactions; By coordinating the interaction between the facial expression control module, the voice interaction module, and the motion control module, the intelligent agent can perform physical tasks while simultaneously providing real-time voice narration and facial expressions, thus achieving multimodal collaboration.

[0119] In this system, the outputs of each module are normalized and used as standardized inputs for subsequent modules, thus forming a progressive generation chain. The normalized text prompts output by the text parsing module serve as input to the image generation module; the normalized 2D image output by the image generation module serves as input to the 3D modeling module; the normalized 3D model output by the 3D modeling module serves as input to the dynamic structure module; the dynamic 3D model output by the dynamic structure module serves as the basis for the expression control and motion control modules; the expression control model output by the expression control module serves as input to the voice interaction module; the multimodal interaction model output by the voice interaction module serves as input to the motion control module; the entity motion control structure output by the motion control module serves as the execution basis for the task reasoning module; the physical action sequences generated by the task reasoning module drive the execution of the motion control module; and finally, the multimodal control center integrates all modules into a complete embodied intelligent agent. This progressive structure ensures the consistency and traceability of multimodal information during transmission.

[0120] In some embodiments, the system can be deployed in a distributed or modular manner according to application requirements, realizing a generation process of partial progression or complete link. For example, in actual robot application scenarios, the voice interaction module, expression control module, and motion control module can be deployed on different smart terminals. Through the communication protocols and data interfaces between modules, local embodied capabilities can be built to form a "local embodied intelligent unit" with independent operating capabilities.

[0121] This system not only supports a full-link generation mode but also allows some modules to run independently. For example, only the text parsing module, image generation module, 3D modeling module, dynamic structure module, and expression control module can be enabled to complete the generation process from text to a 3D image; alternatively, the expression control module, voice interaction module, motion control module, task reasoning module, and multimodal control center can be enabled as a robot control link to realize entity execution tasks. Therefore, this system has high configurability and customizability, and the generation path can be tailored or internal modules replaced according to actual application needs to achieve different types of intelligent agent construction tasks.

[0122] Through the modular design and unified scheduling of the multimodal control center, this system realizes a continuous and progressive process from natural language input to embodied intelligent agent generation. It overcomes the technical problem of lack of structured connections between modalities in traditional multimodal generation systems, and realizes unified representation, logical connection and dynamic collaboration of cross-modal information. This provides a systematic, scalable and practical technical foundation for the construction of embodied artificial intelligence systems.

[0123] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A somatic intelligence generation method based on an N-ary grid progressive mechanism, characterized in that, N=9, the method includes: First grid: Parses natural language descriptions and generates standardized text prompts; Second grid: Generate a standardized two-dimensional image based on the standardized text prompts; Third grid: Based on the normalized two-dimensional image, a standardized three-dimensional model is reconstructed; Fourth grid: Define joint motion parameters for the standardized 3D model to generate a dynamic 3D model; Fifth grid: Based on the dynamic 3D model, establish a mapping between semantics or speech and facial geometric structure driving parameters to generate an expression control model; The sixth grid integrates speech recognition, understanding, and synthesis modules, and aligns them with the facial expression control model to generate a multimodal interaction model; The seventh grid: Based on the joint motion parameters in the multimodal interaction model, virtual actions are mapped to entity driving units to generate entity motion control structures; The eighth grid: Based on the aforementioned entity motion control structure, semantic reasoning and decomposition of user task instructions are performed to generate a sequence of physical actions and drive their execution; The ninth grid integrates the outputs of the above steps into a multimodal control center, forming a complete embodied intelligent agent through data interaction and logical collaboration; The outputs of each step are standardized and used as standardized inputs for subsequent steps, forming a progressive generation chain.

2. The embodied intelligence generation method based on N-gird progressive mechanism according to claim 1, characterized in that, The generation of the normalized two-dimensional image includes: The standardized text prompts are received as input, and the semantic information in the standardized text prompts is mapped to the image generation process using a multimodal alignment algorithm. Based on the normalization constraints, multiple two-dimensional images corresponding to the text semantics are generated. The normalization constraints include the standardization of color, pose, and structural contour, so that the generated two-dimensional image has clear topological structure information and can serve as an effective input standard for subsequent three-dimensional modeling steps. One of the multiple two-dimensional images is selected as the normalized two-dimensional image for subsequent three-dimensional modeling.

3. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 1, characterized in that, Based on the normalized two-dimensional image, a normalized three-dimensional model is reconstructed, including: The normalized two-dimensional image is subjected to feature analysis to extract texture, pose, and depth information. The extracted texture, pose, and depth information are converted into three-dimensional spatial data using a 3D reconstruction algorithm, and spatial reconstruction is performed to generate an initial 3D model; the initial 3D model includes basic geometry and topology. The initial 3D model is standardized to obtain a standardized 3D model; The standardization process includes uniformly processing the model's proportions, topology, and spatial parameters, and recording the model's shape proportions, structural hierarchy, and surface texture information. The standardized 3D model has topological integrity and facilitates joint definition to meet the needs of subsequent steps in embedding motion parameters at joints, connection points, or key structures.

4. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 1, characterized in that, Define joint motion parameters for the standardized 3D model to generate a dynamic 3D model, including: Structural analysis is performed on the standardized 3D model to identify the hierarchical relationships and movable node regions of the model; Define motion nodes in the data structure corresponding to the movable node region; Motion constraints are embedded on the motion nodes, and joint motion parameters are defined to generate a parametric 3D model; A skeleton control chain is introduced into the topology of the parameterized 3D model to establish a mapping relationship from joint motion parameters to geometric deformation, generating a dynamic 3D model with motion degrees of freedom description; the dynamic 3D model includes joint motion parameters, hierarchical dependency relationships and physical constraints, and can directly receive subsequent control signals to realize posture changes and action execution.

5. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 4, characterized in that, A skeleton control chain is introduced into the topology of the parametric 3D model to establish a mapping relationship from joint motion parameters to geometric deformation, including: Based on the motion nodes defined in the parametric 3D model, a skeletal system corresponding to the model's geometry is constructed, which contains multiple skeletal nodes. Each skeletal node corresponds to a movable part of the model and is associated with a motion node. The multiple skeletal nodes are organized into a parent-child hierarchical structure, and the motion of the parent node is transmitted to the child node. The geometric mesh of the parameterized 3D model is bound to the bone system, and the weights of each vertex of the geometric mesh are assigned to each bone node. Associate the joint motion parameters with the corresponding bone nodes and set the mapping rules between them; When the joint motion parameters change, the position and orientation of the affected bone nodes are calculated according to the parent-child hierarchical structure. The position of each vertex of the geometric mesh is recalculated in combination with the weights to generate geometric deformation, thereby realizing the mapping relationship from joint motion parameters to geometric deformation.

6. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 1, characterized in that, Based on the dynamic 3D model, a mapping between semantics or speech and facial geometric driving parameters is established to generate an expression control model, including: Based on the dynamic 3D model, the facial geometry that can be driven is determined; The preset semantic-expression mapping rule library is invoked to map and bind the received semantic signals or voice input with the driving parameters of the facial geometry, thereby establishing a mapping relationship between semantic or voice input and driving parameters; Based on the mapping relationship, semantic signals or voice input are converted into control commands and used to drive the facial geometry. The parameters of the facial geometry are adjusted according to the control command to generate corresponding facial expressions in three-dimensional geometry, and an expression control model is output. The facial expressions include at least one of lip movements, facial expressions, and head posture changes. The expression control model can achieve visual feedback and emotional expression based on semantics or speech.

7. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 1, characterized in that, Integrating speech recognition, understanding, and synthesis modules, and aligning them with the facial expression control model, a multimodal interaction model is generated, including: A voice interaction processing link consisting of speech recognition, semantic understanding and speech synthesis modules is constructed to realize closed-loop interaction of voice input recognition, semantic parsing and voice output; The voice interaction processing link is connected to the facial expression control model to establish a mapping relationship between the output of the voice interaction processing link and the facial expression driving parameters of the facial expression control model. This enables the speech recognition result to trigger the facial expression control model to generate corresponding facial expressions after semantic understanding, and / or the speech synthesis output to keep synchronized with the lip movements of the facial expression control model. The docked voice interaction processing link and facial expression control model are integrated into a unified multimodal interaction model, which enables the multimodal interaction model to have voice interaction capabilities and realize the linkage between voice and facial expressions.

8. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 1, characterized in that, Based on the joint motion parameters in the multimodal interaction model, virtual actions are mapped to entity driving units to generate an entity motion control structure, including: Based on the joint motion parameters in the multimodal interaction model, determine the range of motion, dependency level, and degree of freedom of each joint; Based on joint motion parameters, physical drive units are configured at the joints of the physical device corresponding to the multimodal interaction model; Based on the joint motion parameters and the physical characteristics of the physical drive unit, a parameter mapping relationship between virtual joint movements and physical drive signals is established. The parameter mapping relationship exists in the form of a mapping table or a conversion function, which is used to convert virtual motion commands into corresponding physical drive signals. The configured entity drive unit and the parameter mapping relationship are integrated into an entity motion control structure; the entity motion control structure is used to receive virtual motion commands, convert them into physical drive signals through the parameter mapping relationship, and send them to the corresponding entity drive unit to drive the entity joint movement.

9. The embodied intelligence generation method based on an N-grid progressive mechanism according to claim 1, characterized in that, Based on the aforementioned entity motion control structure, semantic reasoning and decomposition of user task instructions are performed to generate a sequence of physical actions and drive their execution, including: The task instructions input by the user are parsed and converted into a task semantic structure, which includes at least one of an intent unit, a target object, and a location description. Based on the task semantic structure, the task is decomposed into multiple sub-steps that are executed sequentially, generating a sequence of physical actions; each sub-step corresponds to a virtual joint action and its execution parameters. Based on the parameter mapping relationship established in the entity motion control structure, the virtual joint actions corresponding to each sub-step in the physical action sequence are converted into physical drive signals, and the physical drive signals are sent to the corresponding entity drive unit to drive the entity motion control structure to execute the physical action sequence in sequence.

10. An embodied intelligence generation system based on an N-grid progressive mechanism, characterized in that, N=9, the system includes: First Grid: Text Parsing Module, used to parse natural language descriptions and generate standardized text prompts; Second grid: Image generation module, used to generate standardized two-dimensional images based on standardized text prompts; The third grid: 3D modeling module, used to reconstruct a standardized 3D model from a normalized 2D image; The fourth grid: Dynamic structure module, used to define joint motion parameters of the standardized 3D model and generate a dynamic 3D model; The fifth module: the expression control module, is used to establish a mapping between semantics or speech and facial geometric structure driving parameters based on the dynamic 3D model, and generate an expression control model; The sixth module: the voice interaction module, which integrates the speech recognition, understanding and synthesis modules, and aligns with the facial expression control model to generate a multimodal interaction model; The seventh module: Motion control module, used to map virtual actions to physical drive units based on joint motion parameters in the multimodal interaction model, and generate physical motion control structure; The eighth module: Task reasoning module, used to perform semantic reasoning and decomposition of task instructions based on the entity motion control structure, generate physical action sequences and drive execution; The ninth grid: a multimodal control center, used for unified data management and logical connection of various modules to form an embodied intelligent agent with a closed loop of cognition, interaction, action, and operation; The outputs of each module are standardized and used as standardized inputs for subsequent modules, forming a progressive generation chain.

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