Man-machine mutual teaching system and skill transaction method

Abstract

The invention belongs to the technical field of robots, and discloses a man-machine mutual teaching system, which comprises a humanoid robot, and is characterized in that the humanoid robot is provided with a multi-airbag bionic muscle system and an acupuncture point internal and external sensing network, is provided with a preset basic function module, is provided with a mixed learning framework, and is used for integrating end-to-end learning and multi-modal decomposition learning capabilities; the humanoid robot is further provided with a kappa coefficient library, a skill perception module, a ternary skill characterization module, a skill reproduction module, a skill optimization module, a mixed skill library, an isomorphic skill transfer module, a heterogeneous skill adaptation module, a staged skill transaction platform, a coevolution module and an AI intelligent optimization engine. The advantages of end-to-end learning and multi-mode decomposition learning are integrated through a mixed learning framework, the learning efficiency is improved, and the limitation of a single learning normal form is solved; the kappa coefficient library realizes systematic management of force control parameters, provides optimal kappa value configuration for different skills, and greatly improves motion flexibility and accuracy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of robots, and particularly relates to a man-machine mutual teaching system and a skill transaction method. BACKGROUND

[0002] In the field of robot technology, with the rapid development of artificial intelligence technology, humanoid robots gradually show a wide application prospect.

[0003] However, traditional humanoid robots have many limitations in skill learning, representation, reproduction and transaction. For example, a single learning paradigm often cannot meet the needs of different skill learning, resulting in low learning efficiency; the skill representation method is single and lacks flexibility, making it difficult to effectively capture and represent complex skills; there is a lack of precise force control and natural and smooth interactive experience in the skill reproduction process; and the lack of a skill transaction platform limits the sharing and transaction of humanoid robot skills. SUMMARY

[0004] To overcome the above technical problems, the application provides a man-machine mutual teaching system and a skill transaction method.

[0005] The application adopts the following technical solutions: The man-machine mutual teaching system comprises a humanoid robot, characterized in that the humanoid robot is equipped with a multi-airbag bionic muscle system and an acupoint internal and external perception network, a preset basic function module, a hybrid learning framework for integrating end-to-end learning and multi-modal decomposition learning ability, a kappa coefficient library, a skill perception module, a ternary skill representation module, a skill reproduction module, a skill optimization module, a hybrid skill library, an isomorphic skill transfer module, a heterogeneous skill adaptation module, a phased skill transaction platform, a co-evolution module and an AI intelligent optimization engine.

[0006] The skill transaction method of the man-machine mutual teaching system comprises the following stages: a basic ability initialization stage, a learning strategy selection stage, a skill input stage, a ternary skill representation stage, a kappa coefficient configuration stage, a hybrid skill library storage stage, a skill reproduction and optimization stage, an AI optimization and co-evolution deepening stage, an isomorphic skill transfer stage, a skill asset evaluation stage, an isomorphic skill transaction execution stage, a heterogeneous skill adaptation research and development stage, a heterogeneous skill transaction platform expansion stage, a heterogeneous skill transaction execution stage and a skill ecological prosperity stage.

[0007] Compared with the prior art, the application has the following beneficial effects: 1. The hybrid learning framework of the application integrates the advantages of end-to-end learning and multi-modal decomposition learning, improves learning efficiency and solves the limitations of a single learning paradigm; the kappa coefficient library realizes systematic management of force control parameters and provides optimal kappa value configuration for different skills, greatly improving the flexibility and accuracy of actions.

[0008] 2. The ternary skill representation system of the present application combines the advantages of neural network representation and symbolic representation, retaining the generalization ability and implicit knowledge capture ability of neural networks, and having the interpretability and editability of symbolic representation; the combination of preset basic functions and dynamic learning ability ensures basic stability while realizing high scalability; the mixed skill library realizes unified management of multiple representation forms, improving skill storage and utilization efficiency. DETAILED DESCRIPTION

[0009] The examples of the embodiments of the present application are described in detail below, and the raw materials and equipment used, unless specified, can be purchased from the market or are commonly used in the art. The methods in the embodiments, unless specified, are conventional methods in the art. The following examples are illustrative and are used to explain the present application, and cannot be understood as limiting the present application.

[0010] The human-computer interaction system includes a humanoid robot, the humanoid robot is equipped with a multi-airbag bionic muscle system and an internal and external acupoint perception network, the connection points of each airbag are different, the length of the force arm generated is different, the smoothness of the action is realized by the linkage of multiple airbags through inflation or deflation, each airbag is independent, and inflation or deflation can be performed according to the action demand and the length of the assembled force arm to realize seamless connection of force and smooth operation of action, the more regular and more orderly the airbags or connection points are arranged, the smoother the action of airbag inflation / deflation is, each airbag is hinged to each other, and the hinge limit is 30° in the left and right directions along the central axis.

[0011] The humanoid robot is provided with a mixed learning framework for integrating end-to-end learning and multi-modal decomposition learning ability, and is also provided with a kappa coefficient library, a skill perception module, a ternary skill representation module, a skill reproduction module, a skill optimization module, a mixed skill library, an isomorphic skill transfer module, a heterogeneous skill adaptation module, a phased skill transaction platform, a co-evolution module and an AI intelligent optimization engine.

[0012] The mixed learning framework includes: an end-to-end learning engine that directly learns a complete skill sequence through a neural network; Multi-modal decomposition learning engine supports structured component decomposition learning of skills; learning strategy decision maker automatically selects the best learning path based on skill characteristics; whole method learning unit directly learns whole skills through complete demonstration; decomposition method learning unit learns complex skills by decomposing them into basic components; grouping method learning unit learns skills by grouping them by function or body part; progressive method learning unit learns skills step by step from simple to complex; contrastive learning unit understands skill points through positive and negative examples; scenario-based learning unit learns skills in specific scenarios; learning mode fusioner optimizes end-to-end and decomposition learning; knowledge transfer bridge supports experience sharing between different learning strategies.

[0013] Ternary skill representation module includes: neural network representation engine uses deep neural networks to encode implicit skill knowledge; standardized representation engine converts skills into a universal standard format; non-standardized representation engine preserves native representations of specific robot platforms; representation mode selector selects the optimal representation combination based on skill characteristics and use scenarios; hybrid representation processor supports collaborative use of multiple representation modes; representation conversion bridge establishes mapping relationships between different representation forms; metadata annotator adds uniform metadata index to different representation modes; neural-symbol fusion mechanism integrates the advantages of connectionism and symbolic representation.

[0014] Mixed skill library includes: neural network skill storage area stores deep neural network encoded skill models; standard skill storage area stores fully standardized representation skills; non-standard skill storage area stores platform-specific non-standardized representation skills; hybrid skill storage area stores composite skills with multiple representation combinations; scene-state-skill joint representation model associates skills with environmental scenarios and robot states; hierarchical skill structure includes basic skill layer, combined skill layer and complex skill layer; multi-dimensional retrieval system supports multi-path retrieval by representation type, application scenario and skill characteristics; version control system tracks skill evolution and optimization history; data security encryption mechanism protects skill data integrity and rights; skill metadata database stores skill source, contributors, evaluation and usage statistics and other auxiliary information.

[0015] Factory preset basic function module includes: basic action library contains basic actions such as walking, grabbing and balancing; Basic perception processing includes basic data processing of vision, hearing and touch; basic interaction capability supports basic command understanding and feedback; safety control mechanism ensures motion safety and emergency stop; basic learning capability supports simple imitation learning; hardware self-calibration mechanism automatically adapts to individual differences and hardware characteristics.

[0016] The kappa coefficient library includes: a basic kappa value set, a default kappa value range of different basic actions; a professional skill kappa value set, an optimized kappa value configuration of various professional skills; a personalized kappa value set, a kappa value adjustment for specific user habits; A kappa value dynamic mapping table, a kappa value corresponding relationship of different environments and tasks; a kappa value learning model, optimizing the selection of kappa values through historical data; a kappa value combination strategy, a joint kappa value configuration scheme of multiple parts; a kappa value evolution tracker, recording the change trajectory of the kappa value in the skill optimization process; a context adaptability kappa value adjuster, real-time fine-tuning of the kappa value according to environmental feedback.

[0017] The skill reproduction module includes: an action planning unit that plans the action sequence of skill reproduction; a kappa coefficient adjustment unit that dynamically adjusts the torque amplification rate according to skill requirements; a three-force source cooperation unit that controls the cooperative work of the ground, waist, and spine three-force sources; a quality evaluation unit that evaluates the accuracy and effectiveness of the reproduced skill; an adaptive execution unit that adjusts the execution strategy according to real-time feedback; a failure recovery unit that performs real-time correction when execution deviates; A neural network inference executor that processes neural network represented skill execution; a cross-modal coordinator that coordinates the collaborative execution of different representation methods.

[0018] The skill optimization module includes: a feedback collection unit that collects multi-source effect feedback of skill execution; a parameter tuning unit that adjusts skill parameters based on feedback; an adaptive optimization unit that optimizes skill performance according to different environments; An iterative learning unit that continuously improves skills through multiple practices; a performance bottleneck analysis unit that identifies key factors that limit skill performance; a multi-objective optimization unit that balances efficiency, energy consumption, accuracy, and other multiple objectives; A neural network fine-tuning unit that optimizes the parameters of neural network representation; a swarm intelligence optimizer that optimizes individual skills using collective experience of multiple robots.

[0019] The isomorphic skill transfer module includes: an isomorphic recognition unit that recognizes robots of the same model or architecture; A direct transmission channel for direct skill transmission between the same platform; a parameter fine-tuner that fine-tunes differences between different individuals of the same model; a configuration synchronizer that ensures consistent environment configuration and initial state; a performance verifier that verifies the execution effect of the skill on the target robot; an isomorphic group learning optimizer that optimizes skills using isomorphic robot group data; a neural network weight transfer optimizer that handles cross-instance transfer of neural network representation; a skill calibration feedback loop that fine-tunes transferred parameters through execution feedback.

[0020] The heterogeneous skill adaptation module includes: a robot characteristic analysis unit that analyzes hardware parameters and capability boundaries of different robots; a cross-platform skill converter that converts skill representations between different architecture robots; a parameter mapping engine that establishes mapping of kappa coefficients and control parameters between different robots; a capability verification unit that evaluates the capability of different architecture robots to execute a specific skill; and a transfer optimization unit that optimizes the performance of a skill on a heterogeneous robot. A cross-platform protocol converter ensures compatibility of control instructions between different systems; a neural network architecture adapter adjusts neural network structures to adapt to different platforms; and a skill dimension reduction and reconstructor handles dimension mismatch caused by differences in degrees of freedom of target platforms.

[0021] The phased skill transaction platform includes: an internal transaction system that supports skill exchange between homogeneous robots; A platform expansion module gradually supports more robot platforms; a cross-platform transaction bridge enables skill transactions and conversions between different platforms; a skill display unit displays skill functions and effects through multimedia; a value evaluation system evaluates skill value based on dimensions such as complexity, practicality, and innovation; an intelligent pricing engine dynamically prices based on market demand and skill value; a transaction settlement system supports multiple payment methods and smart contracts; an equity protection mechanism ensures the intellectual property rights and income rights of skill creators; a credit evaluation system rates skill quality and seller credibility; a skill recommendation engine provides personalized recommendations based on user needs and history; a skill portfolio market supports transactions of skill packages and skill portfolios; a skill asset digital certificate system ensures traceability and non-tamperability of skill property rights based on blockchain technology; a revenue distribution smart contract automatically executes multi-party revenue distribution of skill transactions; and a skill value derivative market supports transactions of various rights such as skill usage rights and adaptation rights.

[0022] The co-evolution module includes: a real-time feedback unit that provides immediate feedback during skill execution; a multi-modal interaction unit that supports multi-channel interactions such as vision, voice, and touch; a state sharing mechanism that enables humans to understand the learning state and doubts of robots; a learning strategy adjustment unit that dynamically adjusts learning strategies based on bidirectional feedback; a co-evolution engine that promotes mutual improvement of humans and machines during interaction; a compliant interaction controller that adjusts response compliance during interaction; a human behavior optimization advisor that provides improvement suggestions to humans based on machine learning analysis; an interactive teaching efficiency analyzer that evaluates and optimizes the efficiency of human-machine interactive teaching processes; a teaching experience adapter that adjusts teaching interaction methods according to user characteristics; and a co-creation growth recorder that tracks and records the process of human-machine co-growth.

[0023] The AI intelligent optimization engine includes: a skill analysis engine for deep analysis of the internal structure and performance of skills; a kappa coefficient optimization engine for dynamically optimizing kappa value configuration based on skill characteristics; a learning strategy optimizer for automatically selecting the most suitable learning method for the current skill; a compliance adjuster for optimizing response compliance in human-machine interaction; a performance prediction model for predicting the performance of different kappa value configurations; an adaptive optimization loop for continuously optimizing kappa values and learning strategies; a skill innovation engine for generating new skill solutions through skill combination and mutation; a simulation test environment for verifying optimized and innovative skills in virtual space; a contrastive learning unit for identifying advantageous features by comparing different versions of skills; a neural network architecture search engine for automatically optimizing neural network representation structures; a meta-learning optimizer for learning how to learn new skills faster; a generalized skill abstraction engine for extracting general principles from specific skills, storing and dynamically optimizing the moment magnification parameter set for different skill types; a skill perception module for multi-dimensional perception and recording of human demonstration skills; a ternary skill representation module that supports and integrates neural network representation, standardized representation, and non-standardized representation; a skill reproduction module for enabling humanoid robots to accurately reproduce learned skills; a skill optimization module for continuously optimizing skill performance based on multi-source feedback; a hybrid skill library for systematically storing and indexing skill data in multiple representation forms; an isomorphic skill transfer module for implementing lossless skill transfer between robots of the same type; a heterogeneous skill adaptation module for high-fidelity skill conversion between cross-platform robots; a phased skill trading platform that supports the evolution from isomorphic trading to heterogeneous trading; a co-evolution module for enabling bidirectional learning and mutual improvement between humans and machines; an AI intelligent optimization engine for dynamically optimizing learning strategies, representation methods, and execution parameters.

[0024] The skill trading method includes the following stages: a basic ability initialization stage, a learning strategy selection stage, a skill entry stage, a ternary skill representation stage, a kappa coefficient configuration stage, a hybrid skill library storage stage, a skill reproduction and optimization stage, an AI optimization and co-evolution deepening stage, an isomorphic skill transfer stage, a skill asset evaluation stage, an isomorphic skill trading execution stage, a heterogeneous skill adaptation research and development stage, an extension of the heterogeneous skill trading platform stage, a heterogeneous skill trading execution stage, and a skill ecosystem prosperity stage.

[0025] The basic ability initialization stage includes: a. The robot starts with factory default basic function modules; b. Load basic action library, perception processing capability and safety control mechanism; c. Initialize the basic kappa value set of the kappa coefficient library; d. Calibrate the internal and external acupoint perception network to ensure the accuracy of basic perception; e. Load the pre-trained neural network basic model; f. Perform hardware self-calibration to adapt to individual differences; Learning strategy selection stage includes: a. Analyzing the type, complexity and characteristics of the skill to be learned; b. Evaluating the most suitable combination of learning methods in the blended learning framework; c. Learning strategy decision maker selects between end-to-end learning and multi-modal decomposition learning; d. If multi-modal decomposition learning is selected, further determine the specific learning strategy; e. Prepare the corresponding skill perception and representation strategy; f. Confirm the final learning scheme with humans through the co-evolution module; Skill entry stage includes: a. The system enters the selected learning mode; b. The human demonstrator shows the target skill; c. The skill perception module records action, force and environmental interaction information through the internal and external acupoint perception network; d. Process the perception data according to the selected learning strategy: In end-to-end learning mode, encode the complete skill sequence through neural network; In multi-modal decomposition learning mode, record the skill by component or stage; e. The co-evolution module provides real-time learning state feedback, allowing the demonstrator to adjust the teaching strategy, while recording the decision-making mode and adjustment strategy of human experts, achieving bidirectional learning.

[0026] Ternary skill representation stage includes: a. Representation mode selector evaluates skill characteristics to determine the appropriate combination of representation modes; b. Neural network representation engine encodes skills into deep neural network models to capture implicit knowledge; c. For general basic skills, use standardized representation engine to generate standard representation; d. For platform-specific skills, use non-standardized representation engine to generate native representation; e. For complex skills, hybrid representation processor generates a combination of multiple representation methods; f. Representation conversion bridge establishes mapping relationships between different representations; g. Neural-symbol fusion mechanism integrates the advantages of connectionism and symbolic representation; h. Metadata annotator adds uniform search index and labels to the skill; κ coefficient configuration stage includes: a. Ternary skill representation module analyzes skill characteristics; b. Retrieve κ value configuration of similar skills from κ coefficient library; c. AI intelligent optimization engine adjusts κ value based on skill characteristics; d. Prepare dynamic κ value mapping table for different body parts and action stages; e. Softness adjuster sets appropriate interactive softness parameters; f. κ value evolution tracker initializes to record optimization process; g. Context adaptability κ value adjuster sets sensitivity threshold; Mixed skill library storage stage: a. Store skills in corresponding storage areas according to skill representation type: Neural network representation is stored in neural network skill storage area; Standard representation is stored in standard skill storage area; Non-standard representation is stored in non-standard skill storage area; Multiple representation combinations are stored in the mixed skill storage area; b. Scene-state-skill joint representation model establishes the association between skills and environmental scenarios and robot states; c. Skills are integrated into a hierarchical skill structure according to complexity; d. A multi-dimensional retrieval system is established to support multiple retrieval paths; e. A version control system records initial version information; f. A data security encryption mechanism applies protection measures for skill data; g. A skill meta-database records skill sources and contributor information; Skill reproduction and optimization phase: a. Skill reproduction module activates skill model: For neural network representation, execute through neural network inference executor; For standard / non-standard representation, execute through corresponding interpreter; For mixed representation, execute through cross-modal coordinator; b. The κ coefficient adjustment unit loads optimization parameters from the κ coefficient library; c. The three-force source coordination unit controls the generation and transmission of force; d. The compliant interaction controller ensures the natural flow of the execution process; e. The humanoid robot executes the skill; f. The quality assessment unit assesses the reproduction effect; g. The AI intelligent optimization engine analyzes the execution data and proposes optimization suggestions; h. The skill optimization module adjusts the skill parameters according to the feedback and AI suggestions: For neural network representation, optimize through neural network fine-tuning unit; For standard / non-standard representation, optimize through parameter tuning unit; i. The κ value evolution tracker records the parameter optimization path; j. The mixed skill library updates the optimized skill version.

[0027] AI optimization and co-evolution deepening phase includes: a. AI intelligent optimization engine analyzes skill execution effect and human-machine interaction data; b. κ coefficient optimization engine identifies force control optimization space; c. Neural network architecture search engine optimizes network structure; d. Meta-learning optimizer updates learning strategy parameters; e. Co-evolution engine analyzes skill improvement of human demonstrator; f. Human behavior optimization advisor provides improvement suggestions to demonstrator; g. Co-creation growth recorder updates the history of human-machine joint progress; h. Generalized skill abstraction engine extracts general principles and enriches skill library; Isomorphic skill transfer phase: a. Isomorphic recognition unit confirms that the target robot and the source robot are isomorphic platforms; b. Direct transmission channel establishes data connection between two robots; c. Select transmission strategy according to skill representation type: Neural network representation is processed by neural network weight transfer optimizer; Standard representation is directly transmitted; Non-standard representation is transmitted after checking compatibility; d. The parameter fine-tuner adjusts parameters based on individual differences; e. The configuration synchronizer ensures that the environment configuration matches; f. The performance verifier tests the skill transfer effect; g. The skill calibration feedback loop fine-tunes the transfer parameters through execution feedback; h. The homogeneous group learning optimizer collects multi-machine feedback to further optimize the skills.

[0028] 15. The skill trading method of the human-machine mutual teaching system according to claim 3, characterized in that, in the skill asset evaluation stage: a. the value evaluation system performs multi-dimensional skill analysis: technical complexity evaluation, practicality evaluation, innovation evaluation, and scarcity evaluation; b. Market demand analyzer assesses potential market demand; c. Smart pricing engine generates recommended prices and price ranges; d. Skill asset digital certificate system generates unique skill ID and proof of ownership; e. Revenue distribution smart contract sets the revenue distribution ratio among skill creators, platforms, and contributors; The execution phase of homogeneous skill transactions includes: a. The skill is displayed online on the internal trading system; b. The skill display unit generates a demonstration video and detailed description; c. The skill recommendation engine recommends matching skills to potential buyers; d. Owners of similar robots browse and select skills; e. The buyer places an order and completes the payment; f. The transaction settlement system processes the fund transfer; g. The profit distribution smart contract automatically distributes the profits; h. The skill data package is transmitted to the buyer's robot through a direct transmission channel; i. The homogeneous skill transfer module assists in skill loading and fine-tuning; j. The rights protection mechanism records transactions and usage permissions; k. The credit evaluation system collects buyer feedback and evaluations; The heterogeneous skill adaptation R&D stage includes: a. The heterogeneous skill adaptation module begins to analyze the differences in robot architectures on different platforms; b. The robot feature analysis unit establishes parameter mappings for different platforms; c. The neural network architecture adapter designs a cross-platform network conversion strategy; d. The cross-platform skill converter develops conversion algorithms for specific platforms; e. The parameter mapping engine builds cross-platform parameter correspondences; f. The skill dimensionality reduction and reconstructor handle the degree of freedom mismatch problem; g. The capability verification unit defines the cross-platform capability evaluation criteria; h. The transfer optimization unit develops a cross-platform optimization algorithm; i. The cross-platform protocol converter designs the communication protocol conversion mechanism.

[0029] The heterogeneous skill transaction platform expansion stage includes: a. The platform expansion module gradually accesses new robot platforms; b. The cross-platform transaction bridge establishes transaction channels between different platforms; c. The value evaluation system is updated, and cross-platform compatibility scores are added; d. The intelligent pricing engine adds platform adaptation difficulty factors; e. The skill display unit adds cross-platform compatibility display; f. The rights protection mechanism expands to support cross-platform use authorization; g. The skill asset digital certificate system realizes cross-platform right confirmation; h. The skill value derivative market starts to support the transaction of use rights and adaptation rights; i. The skill combination market develops cross-platform skill kits; The heterogeneous skill transaction execution stage: a. Skills are opened to heterogeneous platforms on the expanded transaction platform; b. Different platform robot owners can browse and purchase skills; c. Buyers select skills and complete payment; d. The heterogeneous skill adaptation module automatically generates a compatible version for the buyer's platform: The neural network representation is converted through the neural network architecture adapter; The standard / non-standard representation is processed through the cross-platform skill converter; e. The transfer optimization unit optimizes cross-platform skill performance; f. The skill dimensionality reduction and reconstructor handles freedom differences; g. Buyer feedback is used for continuous improvement of heterogeneous adaptation algorithms; h. The system gradually establishes a skill conversion knowledge base between different platforms; i. The revenue distribution smart contract adjusts the distribution proportion according to the cross-platform difficulty; The skill ecosystem prosperity stage includes: a. The skill innovation engine promotes derivative innovation based on existing skills; b. The skill combination market supports cross-skill, cross-platform combination products; c. The skill value derivative market develops skill asset securitization products; d. Skill creators, optimizers, and adapters form a professional division of labor; e. The generalized skill abstraction engine extracts general frameworks from a large number of skills; f. The meta-learning optimizer continuously improves the skill acquisition efficiency of the system; g. The skill economy forms a self-sustaining positive cycle; h. Through skill transactions, human experience and skills are digitally saved and spread on a large scale.

[0030] Embodiment one: The human-machine mutual teaching system includes a humanoid robot body, a factory pre-set basic function module, a hybrid learning framework, a kappa coefficient library, a skill perception module, a ternary skill representation module, a skill reproduction module, a skill optimization module, a hybrid skill library, a homogeneous skill transfer module, a heterogeneous skill adaptation module, a phased skill transaction platform, a co-evolution module, and an AI intelligent optimization engine.

[0031] The humanoid robot body adopts a multi-airbag bionic muscle system as the driving method, and an acupoint internal and external perception network is arranged at key positions. The multi-airbag system has inherent compliance and can achieve fine force control; the acupoint internal and external perception network simultaneously monitors environmental forces and internal muscle states, providing comprehensive mechanical perception data. This design is particularly suitable for skill learning, as it can capture subtle force changes and body coordination during human demonstration.

[0032] The hybrid learning framework integrates the advantages of end-to-end learning and multi-modal decomposition learning. The end-to-end learning engine learns skills directly from complete demonstrations through deep neural networks, without the need for manual decomposition, making it particularly suitable for capturing implicit skill knowledge. The multi-modal decomposition learning engine provides various structured learning strategies, including holistic, decomposition, and grouping methods, which adapt to the optimal learning path for different types of skills. The learning strategy decision maker automatically selects the best learning path based on skill characteristics, complexity, and learning goals; the learning mode fusioner realizes the collaborative optimization of the two learning paradigms, allowing simple skills to be quickly learned end-to-end and complex skills to be structured and decomposed, and enabling dynamic switching of strategies during the learning process.

[0033] The ternary skill representation module realizes the organic integration of neural network representation and symbolic representation. The neural network representation engine uses deep neural networks to directly encode skills, which is particularly suitable for capturing implicit knowledge and overall coordination patterns; the standardized representation engine converts skills into a universal platform-independent format, ensuring cross-platform compatibility; the non-standardized representation engine retains the native parameters and optimizations of specific robot platforms, maximizing the performance of specific platforms. The representation mode selector determines which representation method or combination to use based on skill characteristics; the hybrid representation processor supports the collaborative use of multiple representation modes; the neural-symbol fusion mechanism combines the adaptive learning capabilities of neural networks with the interpretability and editability of symbolic representations, breaking through the limitations of single representation methods.

[0034] The κ coefficient library systematically manages the torque amplification rate parameters for different skills and scenarios. The basic κ value set contains the default κ value range for various basic actions, such as walking (0.9-1.1) and fine grasping (0.8-0.85); the professional skill κ value set stores optimized parameters for various professional skills, such as the variation curve for calligraphy creation; the individualized κ value set adjusts to specific user habits; the κ value dynamic mapping table establishes the correspondence between κ values under different environmental conditions; the κ value evolution tracker records the optimization history of parameters, providing a basis for continuous improvement; the situational adaptability κ value adjuster adjusts parameters in real-time based on environmental changes, ensuring the stable performance of skills under different conditions.

[0035] The mixed skill library uniformly manages skill data in various forms of representation. The neural network skill storage area stores deep neural network encoded models; the standard skill storage area stores standardized representations; the non-standard skill storage area stores platform-specific representations; and the mixed skill storage area stores various combinations of representations. The scene-state-skill joint representation model associates skills with environmental scenarios and robot states, ensuring that skills are activated under appropriate conditions; the hierarchical skill structure organizes skills by complexity, supporting progressive learning from basic to advanced; the multi-dimensional retrieval system supports skill retrieval by different dimensions, significantly improving utilization efficiency; and the skill metadata database stores auxiliary information such as source, contributor, and evaluation, supporting skill tracing and credit evaluation.

[0036] The three-level power chain works with the kappa coefficient to achieve precise force control. The three-level power chain is based on the biomechanical principle of "force generated from the ground, controlled by the waist, and transmitted through the spine", which divides force generation and transmission into three levels to achieve efficient and coordinated force transmission. The kappa coefficient adjustment mechanism adjusts the torque amplification ratio dynamically to achieve a full spectrum of control from fine and soft to strong and firm. Combined, they provide a precise mechanical foundation for skill reproduction.

[0037] The isomorphic skill transfer module is used to handle the efficient transfer of skills between robots of the same model. The isomorphic recognition unit confirms platform compatibility; the direct transmission channel establishes efficient data connection; the parameter fine-tuner handles individual differences; the skill calibration feedback loop adjusts parameters through execution feedback to ensure transfer quality; and the isomorphic group learning optimizer continuously optimizes multi-machine data. This mechanism enables the average fidelity of skill transfer between isomorphic robots to exceed 95%, significantly improving skill reuse efficiency.

[0038] The heterogeneous skill adaptation module breaks through the technical barriers of skill conversion between different platforms. The robot characteristic analysis unit deeply analyzes hardware differences; the cross-platform skill converter realizes representation conversion between different architectures; the parameter mapping engine establishes key parameter correspondence; and the skill dimensionality reduction and reconstructor solves the problem of freedom degree mismatch by preserving key features and reconstructing actions to approximately reproduce high-degree-of-freedom skills on low-degree-of-freedom platforms. These innovations enable the average fidelity of cross-platform skill transfer to exceed 85%, significantly expanding the range of skill applications.

[0039] The phased skill transaction platform provides a gradual development path from isomorphic transactions to heterogeneous transactions. Starting from transactions within isomorphic platforms, it gradually expands to different models within the same brand, strategic partner platforms, and finally realizes open multi-platform transactions. The skill asset evaluation and pricing mechanism evaluates skill value from multiple dimensions such as technical complexity, practicality, innovation, and scarcity, and dynamically generates reasonable prices based on market supply and demand. The skill asset digital certificate system and income distribution smart contract ensure the balance of creator rights and multiple interests, providing a solid business foundation for the skill economy.

[0040] Co-evolution module enables true human-robot bidirectional learning. Not only does the robot learn skills from humans, but humans also gain feedback and improvement suggestions from the interaction. Real-time feedback unit provides immediate learning status; multi-modal interaction unit supports rich interaction methods; human behavior optimization advisor provides skill improvement suggestions to humans based on machine learning analysis; co-creation growth recorder tracks the joint progress of humans and robots. This bidirectional learning mechanism improves human-robot mutual teaching efficiency by 30-40%, creating a new paradigm of human-robot co-evolution.

[0041] AI intelligent optimization engine continuously optimizes the performance of the entire system. Skill analysis engine deeply understands skill structure; kappa coefficient optimization engine dynamically adjusts force control parameters; neural network architecture search engine optimizes network structure; meta-learning optimizer improves learning efficiency; generalized skill abstraction engine extracts general principles from specific skills, enriching the skill library. This continuous optimization mechanism ensures that the system performance is continuously improved to adapt to changing demands and environments.

[0042] The skill ecosystem formation process as shown demonstrates the complete closed loop from initial skill creation, optimization, trading to derivative innovation. As the skill library expands and trading activity improves, a self-sustaining positive cycle is formed, ultimately building a complete skill economy ecosystem.

[0043] Embodiment Two: This embodiment takes piano playing skill learning as an example to demonstrate the collaborative application of co-evolution module and ternary skill representation.

[0044] Piano playing is a complex skill that involves high-precision finger movements, force control, and time coordination, while also containing a large number of artistic performance elements that are difficult to describe explicitly. After analyzing the skill characteristics, the learning strategy decision maker selects a hybrid strategy of "neural network end-to-end learning + key component decomposition learning" to balance overall artisticity and technical accuracy.

[0045] During the skill entry stage, a pianist demonstrates the performance of a Mozart sonata. The skill perception module records two types of data simultaneously through the internal and external acupoint perception network: (1) precise physical parameters of finger movements, force, and timing; (2) continuous sequence of overall performance. The neural network representation engine encodes the overall performance style and emotional expression in real time; the multi-modal decomposition learning engine analyzes key technical components such as fingerings, touch methods, and pedal usage at the same time.

[0046] The co-evolution module plays a key role in the teaching process: the robot provides real-time learning state feedback such as "third section left-hand chord connection is not smooth" or "right-hand ornamentation dynamics mastery is insufficient". The pianist adjusts the demonstration method according to these feedback, emphasizing specific difficulties, slowing down the speed, or demonstrating different versions in contrast. At the same time, the co-evolution module analyzes the teaching strategies of the pianist (how to disassemble complex passages, how to gradually increase difficulty), and incorporates these teaching experiences into the learning content. The robot not only learns piano playing skills, but also learns how to teach piano playing methods.

[0047] The tri-skilled representation system generates three complementary representations for piano playing: (1) neural network representation captures overall style, expressiveness, and emotional changes; (2) standardized representation records basic fingering, rhythm, and score logic; (3) non-standardized representation preserves fine parameters specific to the current robot finger flexibility. The kappa coefficient library configures a special parameter set for piano playing, including the base kappa value of each finger and the dynamic change curve, achieving from soft and accurate performance to delicate dynamic gradation.

[0048] The skill reproduction and optimization phase produces an interesting human-robot collaborative effect. After the robot first reproduces the performance, the co-evolution module asks the pianist to evaluate the performance and provide feedback. The pianist points out that the contrast between strong and weak is not sharp enough, the legato is not smooth enough, etc., and the AI intelligent optimization engine adjusts the relevant parameters accordingly. At the same time, the co-evolution module analyzes the pianist's own performance characteristics and finds that the right-hand ornamentation is occasionally uneven, and generates a visual analysis for the pianist's reference. This reciprocal feedback makes the pianist himself also realize and improve this detail, achieving true two-way learning.

[0049] After multiple rounds of optimization, the piano playing skill formed under the support of tri-representation has both high artistic expressiveness and technical accuracy. When this skill is listed on the trading platform, the value assessment system gives a comprehensive score of 9.2 / 10 from three dimensions of technical complexity (9.2 / 10), artisticity (9.5 / 10), and scarcity (8.8 / 10), and the intelligent pricing engine generates a premium recommendation accordingly.

[0050] In the isomorphic skill transfer test, the skill is reproduced on the same model robot with 96% fidelity; in the heterogeneous skill transfer test, the skill reducer and reconstructor reconstruct a version of the skill that retains the core performance but has a lower technical difficulty for a simplified version of the robot with lower finger flexibility, with a fidelity of 87%, greatly exceeding the performance of traditional methods.

[0051] Example Three: This example takes the digital inheritance of traditional Chinese woodworking mortise and tenon technology as an example to demonstrate the collaborative application of group intelligence optimization and skill assetization trading.

[0052] Facing the crisis of intangible cultural heritage transmission, a traditional woodworking organization decides to use a human-robot teaching system to preserve and disseminate mortise and tenon technology. The project invites five master craftsmen from different schools to teach robots. The learning strategy decision maker analyzes the characteristics of mortise and tenon technology and chooses a mixed learning strategy that focuses on decomposition and supplements with the whole method. First, learn to make basic mortise and tenon structures, then learn to assemble whole furniture.

[0053] During the skill entry phase, each master craftsman demonstrates the type of mortise and tenon they are good at (such as dovetail, miter, and bean mortise). The skill perception module records the hand movement precision, force control, tool use skill, and material judgment ability through the internal and external perception network of acupuncture points. The co-evolution module captures the unique "hand feeling" and experience of each master, such as how to adjust the cutting angle according to the wood grain and how to judge the tightness of mortise and tenon. These usually difficult-to-express tacit knowledge is encoded in real-time by the neural network representation engine.

[0054] The ternary skill representation system creates a complete representation for each type of mortise and tenon technology: the neural network representation captures the hand feeling and experience; the standardized representation records the basic geometric structure and process; the non-standardized representation preserves the precise control parameters specific to the air bag muscle system. The kappa coefficient library configures a special dynamic adjustment mechanism for woodworking skills, using a higher kappa value (1.1-1.2) to provide sufficient power during rough machining, and automatically switching to a low kappa value (0.8-0.85) to ensure precise control during fine adjustment.

[0055] After the five masters complete the teaching, the isomorphic group learning optimizer begins to play a key role. The system analyzes and compares the skills of different masters and finds their unique advantages: A master's dovetail angle control is the most accurate, B master's force control is the most uniform, C master's material judgment is the most accurate, D master's tool use is the most efficient, and E master's overall design is the most coordinated. The AI intelligent optimization engine combines these advantages to generate a "comprehensive optimized version" for each type of mortise and tenon technology, while preserving the characteristics of each school. The skill optimization module further fine-tunes the parameters through repeated practice tests of these comprehensive versions.

[0056] After group intelligence optimization, the mortise and tenon technology library formed far exceeds the performance of any single master: the accuracy of mortise and tenon structure is improved by 25%, the production efficiency is improved by 30%, and the ability to adapt to different materials is enhanced by 40%. These optimized skills are stored in the mixed skill library and marked with the contributions of different schools in the version control system. More importantly, the system captures and preserves rare mortise and tenon techniques that are at risk of being lost, making a key contribution to the digital preservation of intangible cultural heritage.

[0057] During the skill asset evaluation phase, a valuation system assesses each mortise and tenon joint technique based on four dimensions: technical complexity, cultural value, scarcity, and practicality. Rare mortise and tenon joint techniques, in particular, received the highest scores due to their exceptional cultural value and scarcity. A skill asset digital certificate system generates a unique ID and digital certificate for each skill, clearly documenting the contributions of each master and protecting intellectual property rights. A profit distribution smart contract establishes a fair profit distribution mechanism to ensure that contributors receive ongoing returns.

[0058] After these mortise and tenon joint techniques were released on a multi-tiered skills trading platform, they quickly attracted widespread attention: traditional furniture manufacturers purchased them to improve product quality; vocational and technical schools used them for teaching; museums purchased them for interactive displays; and even collectors purchased the digital copyrights of these rare mortise and tenon joint techniques as cultural investments. Skills trading not only creates economic value but also enables the modern inheritance and innovative application of traditional crafts.

[0059] The Heterogeneous Skill Adaptation Module further expands the application scope of skills, adapting mortise and tenon joint techniques to different robotic platforms, from industrial robotic arms to household service robots. The Skill Dimensionality Reduction and Reconstructor, specifically targeting simplified platforms with lower degrees of freedom, has developed an entry-level version suitable for beginners, significantly lowering the barrier to skill dissemination.

[0060] This case demonstrates the unique value of this invention in the field of cultural heritage preservation: capturing and integrating the essence of different schools through swarm intelligence optimization; preserving complete skills, including implicit knowledge, through a ternary skill representation system; and enabling the transformation and widespread dissemination of traditional skills through a phased skill trading platform. This model provides a new paradigm for the digital preservation and living transmission of intangible cultural heritage.

[0061] Example 4: This example details the complete evolution of a phased skills trading platform from concept to maturity, demonstrating the formation path of a skills economy ecosystem.

[0062] Phase 1: Skill trading within the same platform Initially, the trading platform only supports skill trading between developers' own humanoid robots of the same model. Skills will primarily utilize non-standard representations to ensure optimal performance on a homogeneous platform. The internal trading system has established basic functional processes, organizing and displaying skills into three categories: basic skills, professional skills, and artistic skills.

[0063] The skill asset evaluation system is launched in its initial version, evaluating skill value based on technical complexity, practicality, and innovation. The intelligent pricing engine uses a hybrid pricing strategy of cost orientation and market reference to provide reasonable price recommendation intervals for creators. The skill asset digital certificate system, based on distributed ledger technology, generates a unique ID and ownership proof for each skill, ensuring the rights of skill creators.

[0064] The isomorphic skill transfer module ensures high-quality transfer of skills between robots of the same model, with an average fidelity of 96%. The isomorphic group learning optimizer collects usage feedback to continuously improve popular skills, forming a virtuous iterative cycle. At this stage, the platform accumulates about 500 skills, with monthly transaction volume reaching 1000 times, preliminarily verifying the commercial feasibility of skill trading.

[0065] Second stage: Skill trading between different models of the same brand The platform expands to support skill trading between robots of the same brand but different models. These robots have differences in hardware configuration but similar basic architecture. This stage begins to introduce ternary skill representation, selecting the most suitable representation method or combination based on skill characteristics and target platform.

[0066] The representation conversion bridge becomes a key component, responsible for establishing parameter mapping relationships between different models. Skills from high-end models can be simplified for use in basic models, while basic model skills can be optimized for use in high-end models. The skill asset evaluation system is upgraded to include a "cross-model compatibility" dimension, giving higher evaluations to skills with strong adaptability.

[0067] Skill trading data shows that skills with multiple representation methods receive higher evaluations and more purchases, with an average unit price 30-40% higher than single-representation skills. This market feedback verifies the commercial value of ternary skill representation, prompting more skill creators to adopt multiple representation methods. The platform's skill quantity grows to 2000, with monthly transaction volume exceeding 5000 times.

[0068] Third stage: Strategic partner platform skill trading The platform takes its first step in cross-brand cooperation, establishing skill trading channels with 2-3 strategic partners. These partners use different hardware architectures and control systems, requiring deep technical cooperation and business synergy.

[0069] The heterogeneous skill adaptation module is officially launched, developing conversion algorithms for specific partner platforms. The neural network architecture adapter handles the cross-platform migration of neural network representations through knowledge distillation technology; the parameter mapping engine establishes corresponding relationships for key parameters such as kappa coefficients; and the skill dimensionality reduction and reconstructor solves the adaptation problem between platforms with different degrees of freedom.

[0070] The cross-platform transaction bridge connects different platform transaction systems, supporting unified accounts and settlements. The skill demonstration unit adds cross-platform compatibility identification, clearly showing the fidelity expectations of skills on different platforms. The revenue distribution smart contract is upgraded to automatically adjust the distribution ratio based on cross-platform adaptation difficulty, balancing the interests of originators, platforms, and adaptation contributors.

[0071] This phase introduces the concept of skill value derivatives, starting to support independent trading of various rights such as skill usage rights and adaptation rights. The total number of skills reaches 5000, with monthly transaction volume exceeding 20000 times, forming a scale effect. Skill sharing income becomes a major source of income for some professional skill developers, proving the sustainability of the skill economy model.

[0072] Fourth phase: Open multi-platform skill transaction Based on the successful experience of the previous three phases, the platform is fully open, supporting access to any robot platform that meets technical standards. The platform releases complete technical documents including ternary skill representation specifications, skill evaluation standards, and API interfaces, allowing third-party development of adaptation modules.

[0073] The neural network representation engine and architecture adapter open source their basic components, promoting community participation; standardized representation becomes an industry open standard; heterogeneous skill adaptation modules adopt a plug-in architecture, supporting rapid expansion of new platform adaptation capabilities. The skill asset evaluation system is fully automated, using machine learning algorithms to extract value models from historical transaction data to provide accurate valuations.

[0074] The skill economy ecosystem forms a complete closed loop, including: Skill creators: focus on developing and optimizing original skills Skill adaptation experts: focus on cross-platform skill conversion Skill combination innovators: create new combination products based on existing skills Skill investors: buy potential skill asset rights Skill educators: develop training courses using skill libraries Platform service providers: provide technical support and transaction services The fourth phase is characterized by a burst of derivative innovation. Skill transactions are no longer limited to the direct buying and selling of single skills, but have formed a rich derivative model: skill package subscriptions, skill adaptation authorizations, skill asset tokenization, and skill creator IP valuations. The generalized skill abstraction engine extracts frameworks from a vast number of skills, promoting knowledge transfer between skills and cross-disciplinary innovation.

[0075] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely divergences of the principles and application of the present application. Numerous modifications, changes, variations, substitutions, and equivalents will occur to those skilled in the art without departing from the spirit and scope of the present application as defined by the following claims and their equivalents.

Claims

1. A human-machine mutual teaching system, comprising a humanoid robot, characterized in that: The humanoid robot is equipped with a multi-airbag bionic muscle system and an internal and external acupoint perception network, preset basic functional modules, and a hybrid learning framework for integrating end-to-end learning and multimodal decomposition learning capabilities. The humanoid robot is also equipped with a κ coefficient library, a skill perception module, a ternary skill representation module, a skill reproduction module, a skill optimization module, a hybrid skill library, a homogeneous skill transfer module, a heterogeneous skill adaptation module, a phased skill trading platform, a collaborative evolution module and an AI intelligent optimization engine.

2. The human-machine mutual teaching system according to claim 1, characterized in that: The connection points of each airbag are different, and the length of the lever arm generated is different. Smooth movement is achieved by inflating or deflating multiple airbags in conjunction with each other. Each airbag is independent and can be inflated or deflated in sequence according to the movement requirements and the length of the assembly lever arm to achieve seamless connection of force and smooth operation of the movement. The more regular and neatly arranged the airbags or connection points are, the smoother the airbag inflation / deflation movement is. Each airbag is hinged to each other, and the hinge limit is 30° in the left and right directions along the central axis.

3. The human-machine mutual teaching system according to claim 2, characterized in that: κ coefficient library, which stores and dynamically optimizes the torque amplification parameter sets for different skill types; Skill perception module, used to perceive and record human-demonstrated skills in multiple dimensions; The ternary skill representation module supports neural network representation, standardized representation and non-standardized representation and realizes the fusion; Skill reproduction module, used to enable humanoid robots to accurately reproduce learned skills; Skill optimization module, used to continuously optimize skill performance based on multi-source feedback; A hybrid skills library, used to systematically store and index skill data in multiple representational forms; Isomorphic skill transfer module, used to achieve lossless skill transfer between robots of the same model; Heterogeneous skill adaptation module for high-fidelity skill conversion between cross-platform robots; A phased skill trading platform supports the evolution from homogeneous to heterogeneous trading; Co-evolution module, used to achieve two-way learning and mutual improvement between humans and machines; AI intelligent optimization engine, used to dynamically optimize learning strategies, representation methods, and execution parameters.

4. The human-computer mutual teaching system according to claim 3, its skill trading method includes the following stages: basic ability initialization stage, learning strategy selection stage, skill entry stage, ternary skill characterization stage, κ coefficient configuration stage, hybrid skill library storage stage, skill reproduction and optimization stage, AI optimization and co-evolution deepening stage, isomorphic skill transfer stage, skill asset evaluation stage, isomorphic skill trading execution stage, heterogeneous skill adaptation R&D stage, heterogeneous skill trading platform expansion stage, heterogeneous skill trading execution stage, and skill ecosystem prosperity stage.

5. The skill trading method of the human-machine mutual teaching system according to claim 4, characterized in that: The basic capability initialization phase includes: a. The robot starts up with the factory-preset basic functional modules; b. It loads the basic motion library, perception processing capabilities, and safety control mechanisms; c. It initializes the basic κ value set in the κ coefficient library; d. It calibrates the internal and external acupoint perception networks to ensure basic perception accuracy; e. It loads the pre-trained neural network basic model; f. It performs hardware self-calibration to adapt to individual differences; The learning strategy selection phase includes: a. Analyzing the type, complexity, and characteristics of the skills to be learned; b. Using the hybrid learning framework to evaluate the most suitable learning method combination; c. The learning strategy decision maker selects between end-to-end learning and multimodal decomposition learning; d. If multimodal decomposition learning is selected, further determining the specific learning strategy; e. Preparing the corresponding skill perception and representation strategy; f. Confirming the final learning plan with humans through the co-evolution module; The skill entry phase includes: a. The system enters the selected learning mode; b. A human demonstrator demonstrates the target skill; c. The skill perception module records movement, force, and environmental interaction information through the internal and external acupoint perception network; d. The perception data is processed according to the selected learning strategy: In the end-to-end learning mode, the complete skill sequence is encoded through the neural network; In a multimodal decomposition learning model, skills are recorded by components or stages; e. The co-evolution module provides real-time feedback on learning status, allowing the demonstrator to adjust the teaching strategy while recording the decision-making patterns and adjustment strategies of human experts to achieve two-way learning.

6. The skill trading method of the human-machine mutual teaching system according to claim 4, characterized in that: The ternary skill representation stage includes: a. The representation mode selector evaluates skill characteristics and determines the appropriate representation mode combination; b. The neural network representation engine encodes the skill into a deep neural network model to capture implicit knowledge; c. For common basic skills, a standardized representation engine is used to generate standard representations; d. For platform-specific skills, a non-standardized representation engine is used to generate native representations; e. For complex skills, a hybrid representation processor generates a combination of multiple representation methods; f. The representation conversion bridge establishes mapping relationships between different representations; g. The neural-symbolic fusion mechanism integrates the advantages of connectionist and symbolic representations; h. The metadata annotator adds a unified search index and tags to the skills; The κ coefficient configuration phase includes: a. The ternary skill representation module analyzes skill characteristics; b. Retrieving κ value configurations for similar skills from the κ coefficient library; c. The AI ​​intelligent optimization engine adjusts the κ value based on skill characteristics; d. Dynamic κ value mapping tables are prepared for different body parts and movement phases; e. The flexibility regulator sets appropriate interactive flexibility parameters; f. The κ value evolution tracker is initialized to prepare to record the optimization process; g. The context-adaptive κ value regulator sets the sensitivity threshold; The hybrid skill library storage phase: a. Store the skills into the corresponding storage area based on the skill representation type: The neural network representation is stored in the neural network skill storage area; The standard representation is stored in the standard skill storage area; Non-standard representations are stored in the non-standard skills storage area; Multiple representation combinations are stored in the mixed skill storage area; b. A scenario-state-skill joint representation model establishes an association between skills, environmental scenarios, and robot states; c. Skills are integrated into a hierarchical skill structure based on complexity; d. A multi-dimensional retrieval system is established to support multiple retrieval paths; e. A version control system records initial version information; f. A data security encryption mechanism applies protection measures to skill data; g. A skill metadata database records skill sources and contributors; Skill Reproduction and Optimization Phase: a. The skill reproduction module activates the skill model: The neural network representation is executed through the neural network inference executor; For standard / non-standard representations, it is executed through the corresponding interpreter; For hybrid representations, collaborative execution is performed through a cross-modal coordinator; b. The κ coefficient adjustment unit loads the optimization parameters from the κ coefficient library; c. The three-force source collaborative unit controls the generation and transmission of force; d. The flexible interactive controller ensures a smooth and natural execution process; e. The humanoid robot performs the skill; f. The quality assessment unit evaluates the reproduction effect; g. The AI ​​intelligent optimization engine analyzes the execution data and provides optimization suggestions; h. The skill optimization module adjusts the skill parameters based on feedback and AI suggestions: The neural network representation is optimized through the neural network fine-tuning unit; For standard / non-standard characterization, optimize through parameter tuning unit; i. The κ value evolution tracker records the parameter optimization path; j. The hybrid skill library updates the optimized skill version.

7. The skill trading method of the human-machine mutual teaching system according to claim 4, characterized in that: The deepening phase of AI optimization and co-evolution includes: a. The AI ​​intelligent optimization engine analyzes skill execution performance and human-computer interaction data; b. The kappa coefficient optimization engine identifies the force-controlled optimization space; c. The neural network architecture search engine optimizes the network structure; d. The meta-learning optimizer updates the learning strategy parameters; e. The co-evolution engine analyzes the skill improvements of human demonstrators; f. The human behavior optimization advisor provides improvement suggestions to demonstrators; g. The co-creation growth recorder updates the process of human-computer joint progress; h. The generalized skill abstraction engine extracts common principles and enriches the skill library. Isomorphic skill transfer phase: a. The isomorphic recognition unit confirms that the target robot and the source robot are isomorphic platforms; b. A direct transmission channel establishes a data connection between the two robots; c. The transfer strategy is selected based on the skill representation type: The neural network representation is processed through a neural network weight transfer optimizer; Standard characterization direct transfer; Non-standard characterization is checked for compatibility before transmission; d. The parameter fine-tuner adjusts parameters based on individual differences; e. The configuration synchronizer ensures that the environment configuration matches; f. The performance verifier tests the skill transfer effect; g. The skill calibration feedback loop fine-tunes the transfer parameters through execution feedback; h. The homogeneous group learning optimizer collects multi-machine feedback to further optimize the skills.

8. The skill trading method of the human-machine mutual teaching system according to claim 4, characterized in that: Skill asset assessment stage: a. The value assessment system conducts multi-dimensional skill analysis: technical complexity assessment, practicality assessment, innovation assessment, and scarcity assessment; b. Market demand analyzer evaluates potential market demand; c. The intelligent pricing engine generates suggested prices and price ranges; d. The skill asset digital certificate system generates a unique skill ID and proof of ownership; e. The profit distribution smart contract sets the profit distribution ratio among skill creators, platforms, and contributors; The execution phase of homogeneous skill transactions includes: a. The skill is displayed online on the internal trading system; b. The skill display unit generates a demonstration video and detailed instructions; c. The skill recommendation engine recommends matching skills to potential buyers; d. Owners of similar robots browse and select skills; e. The buyer places an order and completes payment; f. The transaction settlement system processes the fund transfer; g. The profit distribution smart contract automatically distributes the profits; h. The skill data package is transmitted to the buyer's robot through a direct transmission channel; i. The homogeneous skill transfer module assists in skill loading and fine-tuning; j. The rights protection mechanism records transactions and usage permissions; k. The credit evaluation system collects buyer feedback and evaluations; The heterogeneous skill adaptation R&D stage includes: a. The heterogeneous skill adaptation module begins to analyze the differences in robot architectures on different platforms; b. The robot feature analysis unit establishes parameter mappings for different platforms; c. The neural network architecture adapter designs a cross-platform network conversion strategy; d. The cross-platform skill converter develops conversion algorithms for specific platforms; e. The parameter mapping engine builds cross-platform parameter correspondences; f. The skill dimensionality reduction and reconstructor handle the degree of freedom mismatch problem; g. The capability verification unit defines the cross-platform capability evaluation criteria; h. The transfer optimization unit develops a cross-platform optimization algorithm; i. The cross-platform protocol converter designs the communication protocol conversion mechanism.

9. The skill trading method of the human-machine mutual teaching system according to claim 4, characterized in that: The expansion phase of the heterogeneous skill trading platform includes: a. Platform expansion modules are gradually integrated with new robot platforms; b. Cross-platform transaction bridges establish transaction channels between different platforms; c. The value assessment system is updated to include a cross-platform compatibility score; d. The intelligent pricing engine adds a platform adaptation difficulty factor; e. The skill display unit adds a cross-platform compatibility display; f. The rights protection mechanism is expanded to support cross-platform usage authorization; g. The skill asset digital certificate system realizes cross-platform rights confirmation; h. The skill value derivative market begins to support the trading of usage rights and adaptation rights; i. The skill combination market develops cross-platform skill suites; Heterogeneous Skill Transaction Execution Phase: a. Skills are open to heterogeneous platforms on the expanded trading platform; b. Robot owners on different platforms can browse and purchase skills; c. Buyers select skills and complete payment; d. The heterogeneous skill adaptation module automatically generates a compatible version for the buyer's platform: Neural network representations are transformed via neural network architecture adapters; Standard / non-standard representations are handled through a cross-platform skill converter; e. The transfer optimization unit optimizes cross-platform skill performance; f. Skill dimensionality reduction and reconstruction handle differences in degrees of freedom; g. Buyer feedback is used to continuously improve the heterogeneous adaptation algorithm; h. The system gradually builds a knowledge base for skill transfer between different platforms; i. The profit distribution smart contract adjusts the distribution ratio based on cross-platform difficulty; The skill ecosystem prosperity stage includes: a. The skill innovation engine promotes derivative innovation based on existing skills; b. The skill combination market supports cross-skill and cross-platform combination products; c. The skill value derivatives market develops skill asset securitization products; d. Skill creators, optimizers, and adapters form a professional division of labor; e. The generalized skill abstraction engine extracts a common framework from massive skills; f. The meta-learning optimizer continuously improves the system's skill acquisition efficiency; g. The skill economy forms a positive cycle of self-sustaining growth; h. Large-scale digital preservation and dissemination of human experience and skills are achieved through skill trading.