Robot dexterous hand control method and device based on interference identification and medium

Abstract

The invention relates to the field of dexterous hands of humanoid robots, in particular to a robot dexterous hand control method and device based on interference identification and a medium, and the method comprises the steps that S1, a hand shape sequence is obtained based on task information analysis; s2, generating a corresponding joint state for each hand shape in the hand shape sequence, and obtaining a joint state vector corresponding to each hand shape; s3, all two adjacent hand shapes in the hand shape sequence form a hand shape group; s4, taking the joint state vectors of the two hand shapes of each hand shape group as boundary constraint conditions, and combining interference identification model filtering to generate a plurality of joint transition state vectors; s5, splicing all the joint state vectors and the joint transition state vectors in sequence to obtain a control vector of the dexterous hand; and S6, controlling each joint action of the dexterous hand based on the obtained control vector. Compared with the prior art, the method has the advantages that complex space state calculation is not needed, and the like.

Description

Technical Field

[0001] This invention relates to the field of dexterous hands for humanoid robots, and in particular to a method, apparatus, and medium for controlling a robot's dexterous hand based on interference identification. Background Technology

[0002] Traditional robotic arm control relies on pre-programmed rules, which greatly restricts the development of robots. In recent years, the development of artificial intelligence has provided more flexible possibilities for the motion control of robots. AI-based control methods do not require pre-programmed rules for robotic arm control; instead, they can automatically generate control parameters for each joint. Therefore, when developing robotic arms, it is not necessary to restrict the number of joints to a very low range. Based on this, some robotic arms have formed new robotic arm forms by increasing the number of joints.

[0003] These new types of robotic hands, with a greater number of joints, can perform more flexible tasks and are thus known as dexterous hands. For example, Chinese patent CN107081777A discloses a humanoid dexterous hand based on a shape memory alloy flexible intelligent digital composite structure. The dexterous hand provided has the structural potential for sign language display, and some dexterous hands even have more than 20 joints.

[0004] Existing dexterous hand motion control relies on visual demonstration and spatial calculation. For example, Chinese patent CN119388470A discloses a dexterous hand control method, a dexterous hand control device, a dexterous hand, and a robot. It determines the target angle of the drive motor based on first mapping data and teleoperation data, and then sends the target angle of the drive motor to the drive motor, causing the drive motor to rotate to the target angle, so that the drive motor drives the joint to move, thereby moving the fingers to the target position or rotating the joint to the target angle, thus achieving control of the joint, and then controlling the dexterous hand to perform teleoperation actions. In the control of the dexterous hand, it is necessary to perform complex spatial calculations to realize the rotation angle of each joint. In addition, Chinese patent CN120816500A discloses a dexterous hand general grasping method and device based on single-view vision and demonstration optimization, which realizes the object grasping control of the dexterous hand through artificial teaching combined with spatial constraints.

[0005] In summary, existing dexterous hand control methods rely on successive approximations of spatial constraints to control joint rotation angles. On the one hand, solving spatial constraints requires extremely high computing power, making it difficult to achieve autonomous motion control of the dexterous hand on low-computing-power platforms. On the other hand, the motion control of the entire dexterous hand is relatively rigid and not smooth enough. Summary of the Invention

[0006] The purpose of this invention is to provide a robot dexterous hand control method, device and medium based on interference identification, so as to solve the problem of autonomous control of existing dexterous hands based on low computing power platforms without teaching.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A robot dexterous hand control method based on interferometric identification, comprising:

[0009] Step S1: Obtain task information and parse the hand shape sequence based on the task information;

[0010] Step S2: For each hand shape in the hand shape sequence, generate the corresponding joint states to obtain the joint state vector for each hand shape;

[0011] Step S3: Combine any two adjacent hand shapes in the hand shape sequence into a hand shape group;

[0012] Step S4: Use the joint state vectors of the two hand shapes in each hand shape group as boundary constraints, and combine them with the interference identification model for filtering to generate multiple joint transition state vectors;

[0013] Step S5: Concatenate all joint state vectors and joint transition state vectors in sequence to obtain the control vector of the dexterous hand;

[0014] Step S6: Control the joint movements of the dexterous hand based on the obtained control vectors.

[0015] The joint state includes angles and timestamps, and the joint state vector consists of the angles and timestamps of each joint.

[0016] Step S4 includes:

[0017] Step S4-1: Use the angle of the joint state vector of the two hand shapes in each hand shape group as the boundary constraint condition;

[0018] Step S4-2: Use the extreme value of the difference between the boundary angles of each joint in the boundary constraints as the first time weight for each hand shape group;

[0019] Step S4-3: Based on the first-time weight allocation for each hand shape group and combined with the total task time constraint, generate the allocation time for each hand shape group;

[0020] Step S4-4: Based on the allocation time and boundary constraints of each hand shape group, and combined with the interference identification model filtering, generate multiple joint transition state vectors.

[0021] The process of generating a joint transition state vector for a single hand shape group in step S4-4 includes:

[0022] Step S4-4-1: Determine the number of intermediate state vectors based on the allocation time of the hand group;

[0023] Step S4-4-2: Using the angles in the boundary constraints of the hand group as end values, generate multiple intermediate state vectors with equal angle differences between the end values. The intermediate state vectors are composed of the angles of each joint.

[0024] Step S4-4-3: Concatenate all two adjacent intermediate state vectors to obtain the first feature vector, input it into the trained interference identification model, and obtain the interference identification result. The interference identification model is a binary classification result, and the interference identification result is either the presence of mechanical interference or the absence of mechanical interference.

[0025] Step S4-4-4: Determine whether there is interference between any two adjacent intermediate state vectors. If the determination result is that mechanical interference exists, proceed to step S4-4-5; otherwise, proceed to step S4-4-6.

[0026] Step S4-4-5: Regenerate the intermediate state vector and return to step S4-4-3;

[0027] Step S4-4-6: Generate joint transition state vectors based on all current intermediate state vectors.

[0028] Step S4-4-5 includes:

[0029] Step S4-4-5-1: Take the two intermediate state vectors corresponding to the first feature vector that indicates the presence of mechanical interference and all subsequent intermediate state vectors as intermediate state vectors to be optimized.

[0030] Step S4-4-5-2: Regenerate all intermediate state vectors to be optimized using the optimization algorithm, and return to step S4-4-3.

[0031] The optimization algorithm is either a genetic algorithm or a particle swarm optimization algorithm.

[0032] Step S4-4-6 includes:

[0033] Step S4-4-6-1: Calculate the sum angle difference of each adjacent intermediate state vector:

[0034]

[0035] in: For the first i The intermediate state vector and the first intermediate state vector i +1 sum of angle differences of intermediate state vectors N The number of joints in a dexterous hand. For the firsti +1 intermediate state vectors k The angle values ​​of each joint. For the first i The th intermediate state vector k The angle values ​​of each joint. For the joint state vector of the next hand shape in the corresponding hand shape group, the first... k The angle values ​​of each joint. For the joint state vector of the previous hand shape in the corresponding hand shape group, the first... k The angle values ​​of each joint;

[0036] Step S4-4-6-2: Based on the calculated sum of angle differences between adjacent intermediate state vectors, and combined with the allocation time of the hand shape group, generate the duration corresponding to each adjacent intermediate state vector:

[0037]

[0038] in: For the first i The intermediate state vector and the first intermediate state vector i The duration between +1 intermediate state vectors, when i=1, is taken as the duration between the joint state vector of the next hand shape in the corresponding hand shape group and the first intermediate state vector; when i= M When -1, the time interval between the last intermediate state vector and the joint state vector of the next hand shape in the corresponding hand shape group is taken. M This represents the number of angle differences involved in the calculation.

[0039] Step S4-4-6-3: Generate timestamps for each intermediate state vector based on the duration of each adjacent intermediate state vector, and combine the obtained timestamps and each intermediate state vector to obtain a joint transition state vector, wherein the joint transition state vector is composed of the angle and timestamp of each joint.

[0040] The positive samples used during the training of the interference identification model include:

[0041] First positive sample: obtained based on human hand movements captured by a 3D camera;

[0042] The second positive sample: obtained through simulation editing;

[0043] The negative samples used in training the interference identification model were obtained through simulation editing.

[0044] A robot dexterous hand control device based on interferometric identification includes a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the method described above.

[0045] A storage medium having a program stored thereon, which, when executed, implements the method described above.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. By designing to generate multiple transition state vectors in batches between the joint state vectors of every two hand shapes, and performing mechanical interference verification based on the joint transition state vectors, it is possible to achieve dexterous hand learning and autonomous control without teaching on a low computing power platform without complex spatial calculations.

[0048] 2. By using the first-time weight allocation to obtain the allocation time for each hand shape group, the overall coordination of the movement can be improved.

[0049] 3. The intermediate state vectors do not contain timestamps, and the concatenation of two intermediate state vectors is used as the input to the interferometric identification model. On the one hand, the absence of timestamps allows the interferometric identification model to focus on the joint angle itself, avoiding interference from useless information. On the other hand, compared to directly interferencing a single intermediate state vector, it can increase the time interval between adjacent intermediate state vectors, thereby reducing the number of intermediate state vectors and greatly reducing the computational requirements. Furthermore, since the model input is unified, the differences between adjacent hand shapes in the hand shape sequence can be disregarded, and the number of joint transition state vectors generated between any hand shape group can be unconstrained, which greatly increases the degree of freedom and adaptability, and also ensures that the time interval between adjacent transition vectors is within a suitable range, thereby improving the classification accuracy of the interferometric identification model.

[0050] 4. By using the normalized summation of angle differences to allocate the duration of transition state vectors between joints, the coordination and smoothness of the overall movement can be improved. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the main steps of the method of the present invention;

[0052] Figure 2 This is a schematic diagram of the control vector;

[0053] Where: 101, joint state vector, 102, joint transition state vector. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0055] A robot dexterous hand control method based on interference identification is proposed. By designing a method to generate multiple transition state vectors in batches between the joint state vectors of every two hand shapes, and performing mechanical interference verification based on the joint transition state vectors, a dexterous hand can achieve teaching-free learning and autonomous control on a low-computing-power platform without the need for complex spatial calculations.

[0056] like Figure 1 As shown, it includes:

[0057] Step S1: Obtain task information and parse the hand shape sequence based on the task information;

[0058] Specific task information can be directly input or generated through some coarse control algorithms. In this embodiment, taking sign language teaching as an example, after the user inputs text, the sign language translation software automatically translates it into multiple gestures, including hand shape and position. This application focuses on the hand shape, while the position is completed by the arm joint.

[0059] All the hand shapes in a gesture, arranged in order, form a hand shape sequence.

[0060] Step S2: For each hand shape in the hand shape sequence, generate the corresponding joint states to obtain the joint state vector for each hand shape;

[0061] In this embodiment, the joint state includes angle and timestamp. The joint state vector is composed of the angle and timestamp of each joint. In the initial state, the timestamps are all null values ​​and are assigned by the subsequent process. In addition, under normal circumstances, the task information also includes a total time constraint, which represents the total time required to complete the entire hand shape sequence.

[0062] Step S3: Combine any two adjacent hand shapes in the hand shape sequence into a hand shape group;

[0063] Step S4: Using the joint state vectors of the two hand shapes in each hand shape group as boundary constraints, and combining them with the interference identification model for filtering, multiple joint transition state vectors are generated, specifically including:

[0064] Step S4-1: Use the angle of the joint state vector of the two hand shapes in each hand shape group as the boundary constraint condition;

[0065] Step S4-2: Use the extreme value of the difference between the boundary angles of each joint in the boundary constraints as the first time weight for each hand shape group;

[0066] Step S4-3: Based on the first time allocation weight of each hand shape group and combined with the total task time constraint, generate the allocation time of each hand shape group. At this time, after obtaining the allocation time, the timestamp information of the joint state of each hand shape in the hand shape sequence can be improved.

[0067] Step S4-4: Based on the allocation time and boundary constraints of each hand shape group, and combined with the interference identification model filtering, multiple joint transition state vectors are generated. The process of generating a joint transition state vector for a single hand shape group includes:

[0068] Step S4-4-1: Determine the number of intermediate state vectors based on the allocation time of the hand group;

[0069] Step S4-4-2: Using the angles in the boundary constraints of the hand group as end values, generate multiple intermediate state vectors with equal angle differences between the end values. The intermediate state vectors are composed of the angles of each joint.

[0070] Step S4-4-3: Concatenate all two adjacent intermediate state vectors to obtain the first feature vector, input it into the trained interference identification model, and obtain the interference identification result. The interference identification model is a binary classification result, and the interference identification result is either the presence of mechanical interference or the absence of mechanical interference.

[0071] Positive samples used in training the interference identification model include:

[0072] First positive sample: obtained based on human hand movements captured by a 3D camera;

[0073] The second positive sample: obtained through simulation editing;

[0074] The negative samples used in training the interference identification model were obtained through simulation editing.

[0075] Furthermore, the time interval between the joint state vectors of positive and negative samples during the training of the interference identification model should be similar to the time interval between the pre-configured joint transition state vectors.

[0076] Step S4-4-4: Determine whether there is interference between any two adjacent intermediate state vectors. If the determination result is that mechanical interference exists, proceed to step S4-4-5; otherwise, proceed to step S4-4-6.

[0077] Step S4-4-5: Regenerate the intermediate state vector and return to step S4-4-3;

[0078] In this embodiment, step S4-4-5 includes:

[0079] Step S4-4-5-1: Take the two intermediate state vectors corresponding to the first feature vector that indicates the presence of mechanical interference and all subsequent intermediate state vectors as intermediate state vectors to be optimized.

[0080] Step S4-4-5-2: Regenerate all intermediate state vectors to be optimized using the optimization algorithm, and return to step S4-4-3.

[0081] Generally, optimization algorithms can employ genetic algorithms or particle swarm optimization, and a certain degree of diversity should be ensured to avoid getting trapped in local optima.

[0082] Step S4-4-6: Generate joint transition state vectors based on all current intermediate state vectors, specifically including:

[0083] Step S4-4-6-1: Calculate the sum angle difference of each adjacent intermediate state vector:

[0084]

[0085] in: For the first i The intermediate state vector and the first intermediate state vector i +1 sum of angle differences of intermediate state vectors N The number of joints in a dexterous hand. For the first i +1 intermediate state vectors k The angle values ​​of each joint. For the first i The th intermediate state vector k The angle values ​​of each joint. For the joint state vector of the next hand shape in the corresponding hand shape group, the first... k The angle values ​​of each joint. For the joint state vector of the previous hand shape in the corresponding hand shape group, the first... k The angle values ​​of each joint;

[0086] Step S4-4-6-2: Based on the calculated sum of angle differences between adjacent intermediate state vectors, and combined with the allocation time of the hand shape group, generate the duration corresponding to each adjacent intermediate state vector:

[0087]

[0088] in: For the first i The intermediate state vector and the first intermediate state vector i The duration between +1 intermediate state vectors, when i=1, is taken as the duration between the joint state vector of the next hand shape in the corresponding hand shape group and the first intermediate state vector; when i= M When -1, the time interval between the last intermediate state vector and the joint state vector of the next hand shape in the corresponding hand shape group is taken. M This represents the number of angle differences involved in the calculation.

[0089] Setting the duration using this method allows for adaptive time allocation based on the amount of movement, ensuring smooth and coordinated movements. It uses the proportion of the angle change in the current step relative to the total angle change in the entire transition process; transitions with large joint angle changes are automatically allocated more time, while those with smaller changes are allocated less. This avoids the problems that can arise from mechanically distributing time evenly: movements that are too fast in the small amplitude phases requiring fine adjustments, and too slow in the large amplitude phases. The result is that the speed of the entire hand transition movement is adaptive, non-uniform but highly coordinated, closer to the natural laws of human hand movement, greatly improving the smoothness and anthropomorphism of the movement.

[0090] Furthermore, it ensures the precise satisfaction of the total time constraint. Although each duration is allocated proportionally, normalization based on the total change ensures that the sum of the durations of all adjacent vectors is strictly equal to the allocated time for the entire hand gesture group. This guarantees that the macroscopic time planning of the entire action sequence is precisely controllable and can strictly meet the total time requirements of the task. The algorithm achieves local smoothness without violating global time constraints.

[0091] Finally, normalization achieves a fair trade-off across joints. On one hand, it eliminates the influence of dimensions; different joints may have different ranges of motion and units of measurement. Normalization unifies the angular changes of all joints to the same scale, allowing the contributions of each joint to be added fairly. A joint that rotates 10 degrees but has a total stroke of only 15 degrees will contribute far more than another joint that rotates 10 degrees but has a total stroke of 180 degrees. On the other hand, it focuses on the importance of relative motion, paying attention to the relative magnitude of change of each joint within its own range of motion, rather than absolute angle values. This aligns more with physical intuition; a tiny tremor in one joint may be just as important as a large movement in a less sensitive joint, and normalization captures this difference.

[0092] Step S4-4-6-3: Generate the timestamp of each intermediate state vector based on the duration of each adjacent intermediate state vector, and combine the obtained timestamps and each intermediate state vector to obtain the joint transition state vector, wherein the joint transition state vector is composed of the angle and timestamp of each joint.

[0093] In the above scheme, "model classification and judgment" replaces "complex numerical calculation," and "intelligent time allocation" and "dynamic path optimization" ensure smooth and interference-free movements, ultimately achieving the goal of dexterous, natural, and reliable autonomous control of a robot hand on a low-computing-power platform. Specifically, this is reflected in:

[0094] 1. Significantly reduce computing power requirements and achieve efficient control on low-computing-power platforms.

[0095] Traditional methods rely on complex spatial geometric calculations to avoid mechanical interference, resulting in a heavy computational burden. This solution introduces an interference identification model, transforming the complex spatial computation problem into an efficient binary classification problem. The model only needs to determine whether interference is present or absent in the spliced ​​joint state vectors, greatly simplifying the computation process and making it possible to achieve real-time, autonomous dexterous hand control on embedded platforms or mobile robot bodies with limited computing power.

[0096] 2. Improve the smoothness and coordination of motion planning.

[0097] Intelligent time allocation: By using the extreme values ​​of the changes in the boundary angles of each joint as the weights for time allocation, it ensures that the joints with the largest changes have enough time to complete the movement. This avoids abrupt or uncoordinated overall movements caused by a single joint requiring a large range of rapid motion, making the transitions between hand shapes more natural and smooth.

[0098] Dynamic timestamp allocation: When generating the final joint transition state vector, the time is not simply allocated evenly, but rather dynamically allocated based on the normalized sum angle difference between adjacent intermediate state vectors. This means that transition phases with large joint angle changes are allocated more time, while phases with small changes are allocated less time. This "variable speed" motion planning further enhances the overall smoothness and anthropomorphic coordination of the movement.

[0099] 3. Optimize the efficiency and accuracy of interferometric detection.

[0100] Focusing on key information: The intermediate state vector contains only joint angles and no timestamps. This allows the interference identification model to focus on the joint spatial configuration itself, eliminating the interference of time information and helping to improve the accuracy and generalization ability of the model's judgment.

[0101] Detecting "motion process" rather than "static state": A key innovation of this method is that it concatenates the vectors of two adjacent intermediate states and uses them as model input. Instead of detecting interference from a single static hand shape, it detects whether interference will occur during the brief process of moving from one state to the next. This is more consistent with actual physical processes and can effectively identify situations where there is no interference in the static state but collisions will occur along the motion path, greatly improving the practicality and accuracy of the detection.

[0102] 4. Enhance the robustness and adaptability of the algorithm.

[0103] Local optimization strategy: When interference is detected, instead of completely rejecting the entire algorithm and starting over, the algorithm optimizes only the intermediate state vectors from the first node where interference occurs. This strategy is computationally efficient, avoids the massive computational cost of global search, and allows the algorithm to quickly find interference-free feasible paths, making it more robust.

[0104] Process standardization: Step S4 breaks down the complex hand shape transition problem into a series of standardized sub-steps. This modular design enables the algorithm to handle the transition between any two hand shapes, regardless of specific hand shape differences, and provides strong versatility and adaptability.

[0105] Step S5: Concatenate all joint state vectors and joint transition state vectors in sequence to obtain the control vector of the dexterous hand, such as... Figure 2 As shown, when there are 3 hand shapes in a hand shape sequence, the joint transition state vector 102 and the joint state vector 101 are arranged in order, and all joint transition state vectors 102 located between a certain hand shape group are located between the two joint state vectors 101 of that hand shape group.

[0106] Step S6: Control the joint movements of the dexterous hand based on the obtained control vectors.

[0107] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A robot dexterous hand control method based on interferometric identification, characterized in that, include: Step S1: Obtain task information and parse the hand shape sequence based on the task information; Step S2: For each hand shape in the hand shape sequence, generate the corresponding joint states to obtain the joint state vector for each hand shape; Step S3: Combine any two adjacent hand shapes in the hand shape sequence into a hand shape group; Step S4: Use the joint state vectors of the two hand shapes in each hand shape group as boundary constraints, and combine them with the interference identification model for filtering to generate multiple joint transition state vectors; Step S5: Concatenate all joint state vectors and joint transition state vectors in sequence to obtain the control vector of the dexterous hand; Step S6: Control the joint movements of the dexterous hand based on the obtained control vectors.

2. The robot dexterous hand control method based on interferometric identification according to claim 1, characterized in that, The joint state includes angles and timestamps, and the joint state vector consists of the angles and timestamps of each joint.

3. The robot dexterous hand control method based on interferometric identification according to claim 2, characterized in that, Step S4 includes: Step S4-1: Use the angle of the joint state vector of the two hand shapes in each hand shape group as the boundary constraint condition; Step S4-2: Use the extreme value of the difference between the boundary angles of each joint in the boundary constraints as the first time weight for each hand shape group; Step S4-3: Based on the first-time weight allocation for each hand shape group and combined with the total task time constraint, generate the allocation time for each hand shape group; Step S4-4: Based on the allocation time and boundary constraints of each hand shape group, and combined with the interference identification model filtering, generate multiple joint transition state vectors.

4. The robot dexterous hand control method based on interferometric identification according to claim 3, characterized in that, The process of generating a joint transition state vector for a single hand shape group in step S4-4 includes: Step S4-4-1: Determine the number of intermediate state vectors based on the allocation time of the hand group; Step S4-4-2: Using the angles in the boundary constraints of the hand group as end values, generate multiple intermediate state vectors with equal angle differences between the end values. The intermediate state vectors are composed of the angles of each joint. Step S4-4-3: Concatenate all two adjacent intermediate state vectors to obtain the first feature vector, input it into the trained interference identification model, and obtain the interference identification result. The interference identification model is a binary classification result, and the interference identification result is either the presence of mechanical interference or the absence of mechanical interference. Step S4-4-4: Determine whether there is interference between any two adjacent intermediate state vectors. If the determination result is that mechanical interference exists, proceed to step S4-4-5; otherwise, proceed to step S4-4-6. Step S4-4-5: Regenerate the intermediate state vector and return to step S4-4-3; Step S4-4-6: Generate joint transition state vectors based on all current intermediate state vectors.

5. The robot dexterous hand control method based on interferometric identification according to claim 4, characterized in that, Step S4-4-5 includes: Step S4-4-5-1: Take the two intermediate state vectors corresponding to the first feature vector that indicates the presence of mechanical interference and all subsequent intermediate state vectors as intermediate state vectors to be optimized. Step S4-4-5-2: Regenerate all intermediate state vectors to be optimized using the optimization algorithm, and return to step S4-4-3.

6. The robot dexterous hand control method based on interferometric identification according to claim 5, characterized in that, The optimization algorithm is either a genetic algorithm or a particle swarm optimization algorithm.

7. The robot dexterous hand control method based on interferometric identification according to claim 5, characterized in that, Step S4-4-6 includes: Step S4-4-6-1: Calculate the sum angle difference of each adjacent intermediate state vector: ； in: For the first i The intermediate state vector and the first intermediate state vector i +1 sum of angle differences of intermediate state vectors N The number of joints in a dexterous hand. For the first i +1 intermediate state vectors k The angle values ​​of each joint. For the first i The th intermediate state vector k The angle values ​​of each joint. For the joint state vector of the next hand shape in the corresponding hand shape group, the first... k The angle values ​​of each joint. For the joint state vector of the previous hand shape in the corresponding hand shape group, the first... k The angle values ​​of each joint; Step S4-4-6-2: Based on the calculated sum of angle differences between adjacent intermediate state vectors, and combined with the allocation time of the hand shape group, generate the duration corresponding to each adjacent intermediate state vector: ； in: For the first i The intermediate state vector and the first intermediate state vector i The duration between +1 intermediate state vectors, when i=1, is taken as the duration between the joint state vector of the next hand shape in the corresponding hand shape group and the first intermediate state vector; when i= M When -1, the time interval between the last intermediate state vector and the joint state vector of the next hand shape in the corresponding hand shape group is taken. M This represents the number of angle differences involved in the calculation. Step S4-4-6-3: Generate timestamps for each intermediate state vector based on the duration of each adjacent intermediate state vector, and combine the obtained timestamps and each intermediate state vector to obtain a joint transition state vector, wherein the joint transition state vector is composed of the angle and timestamp of each joint.

8. The robot dexterous hand control method based on interferometric identification according to claim 1, characterized in that, The positive samples used during the training of the interference identification model include: First positive sample: obtained based on human hand movements captured by a 3D camera; The second positive sample: obtained through simulation editing; The negative samples used in training the interference identification model were obtained through simulation editing.

9. A robot dexterous hand control device based on interferometric identification, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-8.

10. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-8.

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