Body-equipped intelligent general robot training and control system

Abstract

The invention provides an intelligent universal robot training and control system, which comprises sensors, intelligent executing mechanisms, a model reasoning platform and a training platform, wherein the model reasoning platform and the training platform are decoupled and separated; a data and communication module is accessed to the sensors and the intelligent executing mechanisms, and isomerous messages of the sensors and the intelligent executing mechanisms of different models are unified; when a sensor / mechanical arm is newly added or replaced, only minimum adaptation is needed, and the upper-layer algorithm logic is not affected; the reasoning platform and the training platform are isolated through the model bridge and the intermediate representation module, and observation data unified by the data and communication module are received; the model reasoning platform is provided with a learning and reasoning module, and the learning of long-time sequence and multi-modal information is enhanced by adopting time sequence modeling of equivariant diffusion EquiDiff in combination with Transform; and a teaching and data quality module is also arranged, and track shaping and quality monitoring are carried out on teaching action data of simulation learning.

Description

Technical Field

[0001] This invention belongs to the field of robot control and imitation learning, and particularly relates to a training and control system for an embodied intelligent general-purpose robot. Background Technology

[0002] In industrial manufacturing, warehousing and logistics, and service robot scenarios, robots need to perform tasks such as grasping, handling, assembly, dispensing, and picking in complex and dynamic environments. Traditional systems typically employ a three-layer architecture of "perception-planning-control." To reduce reliance on manual rules and expert experience, imitation learning is gradually becoming an important technical approach for robot control.

[0003] Existing patent CN119407766B discloses a robot imitation learning method, apparatus, computer device, and readable storage medium. The method includes: acquiring demonstration data sequentially over time and inputting it into a first encoder of a target model for high-dimensional feature extraction to obtain a first target feature; acquiring action instruction information corresponding to the demonstration data; decomposing the first target feature into multiple action blocks based on the action instruction information using a conditional variational autoencoder; sequentially reconstructing the features of the multiple action blocks based on the action instruction information to obtain multiple corresponding first sub-features; concatenating the multiple first sub-features to obtain a second target feature corresponding to the first target feature; predicting the second target feature using a first decoder of the target model to obtain the joint position parameters of the robot's joints at the next moment; and controlling the robot to imitate based on the joint position parameters. This improves the accuracy of robot imitation learning. Summary of the Invention

[0004] Existing robot control methods primarily rely on various specialized algorithms to recognize environmental states. In this process, a separate state recognition algorithm needs to be designed for each type of sensor (such as depth cameras, force sensors, RGB cameras, etc.), and the corresponding control logic and task planning code must be manually written to combine different task objectives and state information, thereby enabling the robot to execute the task. The drawback of this approach is that: Long R&D cycle: It requires repeated development for each task and hardware environment, and lacks rapid adaptation capability; High maintenance costs: Once the mission objectives, hardware, or environment change, some or even all of the control logic needs to be redesigned. Insufficient flexibility: Unable to quickly migrate and deploy to new tasks and scenarios; Meanwhile, existing data acquisition and inference systems are generally tied to specific sensor and robotic arm models. This necessitates significant modifications to the underlying code of both the acquisition and inference systems, and sometimes even rewriting the adaptation modules, when replacing hardware (such as robotic arms from different manufacturers, cameras, or sensors). Specific problems include: Severe hardware dependency: Changing the sensor model requires modification of the underlying code for status acquisition, data parsing, etc. Lack of cross-device reusability: The observation data acquisition modules of different robotic arm models cannot be directly reused; High deployment costs: Each time new hardware is adapted, a lot of code modifications and testing are required, which prolongs the deployment cycle.

[0005] In existing solutions, model inference platforms in the field of robot control often rely on specific model training platforms, with their interfaces and data formats tightly bound. When it's necessary to change the training platform (such as switching to a higher-performance distributed training framework), extensive adaptation and testing of the inference platform is required. This leads to: High migration costs: Replacing the training platform requires refactoring some inference platform code; Limited technology updates: In order to avoid high migration costs, the system is unable to introduce new training platform technologies in a timely manner; Cross-platform collaboration difficulties: Sharing models between different research teams can increase workload due to platform compatibility issues. In robot imitation learning, the quality of the teaching data directly affects the fluency and accuracy of the generated actions of the model; Traditional teaching methods often rely on manual guidance or simple trajectory recording, which has the following problems: Unsmooth motion trajectory: Noise exists in the trajectory acquisition, causing jitter in the generated motion; Insufficient position and velocity control precision: The limited accuracy of the trajectory during the teaching process affects the final execution effect; Lack of industrial-grade stability: It is difficult to apply directly to task scenarios that require high stability and consistency, such as factory logistics and precision assembly.

[0006] In the field of action generation through robot imitation learning, existing technologies often use diffusion models based on the UNet architecture as generators, but their generalization ability and action understanding capabilities in complex tasks are limited. Poor task adaptability: It lacks the ability to migrate across tasks and scenarios and requires a large amount of task-specific data; Limited depth of action understanding: It struggles to capture complex temporal dependencies in high-dimensional, multimodal input data; Insufficient diversity of generated actions: It is prone to pattern collapse, resulting in a lack of variety and flexibility in generated actions.

[0007] To address the aforementioned technical problems, the present invention provides the following technical solution: an embodied intelligent general-purpose robot training and control system, comprising sensors, embodied intelligent actuators, and decoupled model inference platform and training platform: a data and communication module connects to the sensors and embodied intelligent actuators, unifies heterogeneous messages from different types of sensors and embodied intelligent actuators, and decouples the robot from the environmental data monitored by the sensors; a model bridge and intermediate representation module isolate the inference platform from the training platform, receiving the unified observation data from the data and communication module; the model inference platform includes a learning and inference module, employing isovariant diffusion EquiDiff combined with Transformer temporal modeling; it also includes a teaching and data quality module, which performs trajectory shaping and quality monitoring on the teaching action data of imitation learning, and inputs it to the data and communication module. The embodied intelligent actuator can be composed of a robotic arm dexterous hand or a humanoid robot execution module.

[0008] Specifically, the data and communication module uses the topic / service mechanism of the ROS2 bus to insert a data translator node between the sensor's native topics and the upper-layer algorithm. This translates different types of messages from different sensor models into a unified dictionary structure and then into JSON format for transmission.

[0009] Specifically, the model bridge and intermediate representation module isolate the runtime environment of the model inference platform from the training platform environment through a socket, and connect to different training platforms through different model bridge classes.

[0010] Specifically, the model bridge and intermediate representation module send observation data and receive inference results output by the model based on socket technology, and transmit data through the interface with the training platform via the model bridge class.

[0011] Specifically, the learning and reasoning module integrates the EquiDiff equivariant diffusion model with Transformer temporal modeling, uses group convolution to replace standard convolution, introduces symmetry, equivariant priors and sequence modeling capabilities, and integrates multimodal observations and task conditions under the equivariant constraints of the three-dimensional Euclidean group to enhance the learning of long-term and multimodal information and reduce the dependence on task-specific data.

[0012] Specifically, the teaching and data quality module constructs a high-quality teaching and trajectory monitoring process, performs timestamp quality monitoring on the teaching trajectory, and calculates whether the mean and variance of the timestamp frequencies of each sensor collected are within the threshold range.

[0013] Specifically, the trajectory shaping in the teaching and data quality module includes: fixed-period resampling and low-pass filtering of the teaching trajectory; velocity / acceleration / jerk constraints; spline smoothing of the data; and jitter and hysteresis processing of the gripper curve.

[0014] Specifically, the quality monitoring of the teaching and data quality module includes: thresholding the mean and variance of timestamp frequencies for each modality, monitoring the frame loss rate and cross-modal timing deviation, marking or removing unqualified samples, and generating a quality report.

[0015] Specifically, the robotic arm is constructed using servo motors or servo motors with encoders.

[0016] Specifically, the robotic arm has a sampling frequency of ≥200 Hz and an angular resolution of ≤0.1°.

[0017] The beneficial effects of this invention are as follows: A sensor data translator is set up on top of ROS2 to unify and standardize heterogeneous topics, allowing for minimal adaptation when adding or replacing sensors / robotic arms without affecting the upper-level algorithm logic. A data-driven imitation learning path replaces large-scale rule writing, reducing combinatorial explosion of state machines / rule bases under complex tasks; simultaneously, configurable behavioral constraints and abnormal branches are provided at the task level, enhancing adaptability to environmental changes and boundary conditions. A model bridge module is used to manage connections to different training platforms, enabling the system to interface with various training platforms; decoupling training and inference facilitates platform migration and upgrades. An EquiDiff combined with Transformer generative architecture is adopted, introducing symmetry / equivariance priors and sequence modeling capabilities to strengthen the learning of long-term and multimodal information, reduce dependence on task-specific data, and achieve robust transfer across tasks and scenarios. A high-quality teaching and trajectory monitoring process is constructed, performing timestamp quality monitoring on the teaching trajectory, calculating whether the mean and variance of the timestamp frequencies of each collected sensor are within the threshold range, ensuring the quality of the teaching data. Attached Figure Description

[0018] Figure 1 This is a system block diagram of the present invention.

[0019] Figure 2 This is a schematic diagram showing the structural relationship between the modules of this invention.

[0020] Figure 3 This is a flowchart of the data acquisition process.

[0021] Figure 4 This is a flowchart of the model reasoning process. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Example 1: A training and control system for an embodied intelligent universal robot, such as Figure 1 As shown, the system includes sensors, embodied intelligent actuators, and decoupled model inference and training platforms: the data and communication module connects to the sensors and embodied intelligent actuators, unifies heterogeneous messages from different types of sensors and embodied intelligent actuators, and decouples the robot from the environmental data monitored by the sensors; the model bridge and intermediate representation module isolate the inference platform from the training platform, receiving the unified observation data from the data and communication module; the model inference platform has a learning and inference module, which uses EquiDiff combined with Transformer for temporal modeling; it also has a teaching and data quality module, which performs trajectory shaping and quality monitoring on the teaching action data of the imitation learning, and inputs it to the data and communication module.

[0024] like Figure 2 As shown, in this embodiment, the embodied intelligent general-purpose robot body is decoupled from external sensors, model inference platform, and training platform through a data and communication module as an intermediary. The model inference platform and training platform are decoupled and isolated from the intermediate representation module through a model bridge. Under this design, each module can be disassembled, upgraded, maintained, or replaced separately, ensuring the flexibility, versatility, and high performance of the embodied intelligent general-purpose robot.

[0025] In this embodiment, the embodied intelligent actuator is a dexterous hand robotic arm. The robotic arm is constructed using servo motors or servo motors with encoders, and its sampling frequency is ≥200 Hz and angular resolution is ≤0.1°. Although this embodiment uses a dexterous hand robotic arm as an example, the embodied intelligent general-purpose robot training and control system provided in this embodiment can be applied to any embodied intelligent general-purpose robot.

[0026] Because different sensor models differ in timestamp accuracy, topic / message format, coordinate system, and calibration method, existing systems often directly interface with the original topic at the ROS / ROS2 layer, which means that the upper-layer preprocessing and status recognition nodes must be customized according to the model. Once the sensor or robotic arm interface is changed, the data pipeline and algorithm need to be rewritten or significantly modified, resulting in high adaptation costs, poor reusability, and long deployment cycles.

[0027] Meanwhile, since the acquisition side and the reasoning / planning side mostly use raw or semi-structured data for point-to-point communication, there is a lack of unified data semantic specifications and standardized structures. This makes it difficult to directly reuse the observation data modules when crossing robotic arms and projects. System evolution requires repeated adaptation, resulting in high module coupling and low reuse rate.

[0028] Therefore, the data and communication module in this embodiment, based on the topic / service mechanism of the ROS2 bus, inserts a data translator node between the sensor's native topics and the upper-layer algorithm. This transforms different types of messages from different sensor models into a unified dictionary structure and transmits them in JSON format. A sensor data translator is set up on top of ROS2 to unify and standardize heterogeneous topics, ensuring that adding or replacing sensors / robotic arms requires only minimal adaptation without affecting the upper-layer algorithm logic. The data and communication module uniformly connects to heterogeneous sensors and robotic arms, providing standardized observation semantics and decoupling the system from hardware models. Adding or replacing hardware only requires modifying the decoder plugin, without affecting the upper-layer algorithm. The specific data acquisition process is as follows: Figure 3 As shown.

[0029] In existing solutions, model inference platforms in the field of robot control often rely on specific model training platforms, with their interfaces and data formats tightly bound. When it's necessary to change the training platform (such as switching to a higher-performance distributed training framework), extensive adaptation and testing of the inference platform is required. This leads to: High migration costs: Replacing the training platform requires refactoring some inference platform code; Limited technology updates: In order to avoid high migration costs, the system is unable to introduce new training platform technologies in a timely manner; Cross-platform collaboration is difficult: When different research teams share models, platform compatibility issues can increase the workload.

[0030] Because data formats, model export methods, and operator / driver versions are interdependent between the training and inference ends, changing the training framework or hardware platform requires extensive adaptation and integration on the inference side. This leads to high costs for platform migration, version upgrades, and maintenance, and extends the technology update cycle. Therefore, there is an urgent need for a modular inference platform design that allows communication between the platform and the training platform via standardized interfaces, thereby reducing the costs of platform switching and upgrades.

[0031] In this embodiment, the model bridge and intermediate representation module isolate the runtime environment of the model inference platform from the training platform environment via sockets, and connects to different training platforms through different model bridge classes. Since the inference environment and training platform environment are isolated via sockets, there is no need to worry about conflicts between them.

[0032] The model bridge and intermediate representation module send observation data and receive inference results output by the model based on socket technology, and transmit data with the training platform through the model bridge class.

[0033] By using the model bridge module to manage connections to different training platforms, this system gains the ability to interface with various training platforms. Within the model bridge subclasses, each model bridge initializes models for its respective platform and processes observation data and model output according to model requirements. Furthermore, the model bridge isolates the inference environment from the training platform environment via sockets, enhancing its compatibility. The specific model inference process is as follows: Figure 4 As shown.

[0034] In the field of action generation through robot imitation learning, existing technologies often use diffusion models based on the UNet architecture as generators, but their generalization ability and action understanding capabilities in complex tasks are limited. Poor task adaptability: It lacks the ability to migrate across tasks and scenarios and requires a large amount of task-specific data; Limited depth of action understanding: It struggles to capture complex temporal dependencies in high-dimensional, multimodal input data; Insufficient diversity of generated actions: It is prone to pattern collapse, resulting in simple and inflexible generated actions; Because UNet primarily uses spatial convolutions, its ability to fuse and represent long temporal dependencies and multimodal data (such as multi-view video and robotic arm states) is limited. This results in insufficient generalization ability across tasks and scenarios, and a strong dependence on task-specific data volumes, thereby increasing data and annotation costs and reducing transfer efficiency. Therefore, a more advanced model architecture, such as EquiDiff combined with Transformer, is needed to improve generalization and learning capabilities across multiple tasks and scenarios.

[0035] The learning and inference module of this embodiment integrates the EquiDiff equivariant diffusion model and Transformer temporal modeling, uses group convolution to replace standard convolution, introduces symmetry, equivariant priors and sequence modeling capabilities, and integrates multimodal observations and task conditions under the equivariant constraints of the three-dimensional Euclidean group to enhance the learning of long-term and multimodal information and reduce the dependence on task-specific data.

[0036] In existing technologies, teaching data often directly records the controller's raw output without uniform trajectory shaping (such as resampling, filtering, and jerk constraints). Training samples are prone to noise and unbalanced temporal sampling, which leads to motion jitter and large tracking errors during inference and execution. This makes it difficult to meet the industrial requirements for smooth and consistent motion in factory logistics and precision assembly.

[0037] The teaching and data quality module in this embodiment constructs a high-quality teaching and trajectory monitoring process, performs timestamp quality monitoring on the teaching trajectory, and calculates whether the mean and variance of the timestamp frequencies of each sensor collected are within the threshold range.

[0038] The trajectory shaping in the teaching and data quality module specifically includes: resampling the teaching trajectory at fixed periods and low-pass filtering; constraining velocity / acceleration / jerk; smoothing the data with splines; and performing jitter and hysteresis processing on the gripper curve.

[0039] The quality monitoring of the teaching and data quality module includes: thresholding the mean and variance of timestamp frequencies for each modality, monitoring the frame loss rate and cross-modal timing deviation, marking or removing unqualified samples, and generating a quality report.

[0040] This embodiment uses low-cost servo motors (≥200Hz, ≤0.1°) to acquire high-resolution teaching trajectories, and performs resampling, low-pass filtering, velocity / acceleration / jerk limiting, and spline smoothing; it also performs threshold detection and reporting on the mean / variance of multimodal timestamp frequencies, frame loss rate, and cross-modal time difference to ensure industrial-grade smoothness and consistency.

[0041] Example 2: An embodied intelligent general-purpose robot training and control system includes sensors, a robotic arm, and decoupled model inference platform and training platform: a data and communication module connects to the sensors and robotic arm, unifying heterogeneous messages from different types of sensors and robotic arms; a model bridge and intermediate representation module isolate the inference platform from the training platform, receiving the unified observation data from the data and communication module; the model inference platform has a learning and inference module, employing EquiDiff combined with Transformer temporal modeling; it also has a teaching and data quality module, which performs trajectory shaping and quality monitoring on the teaching action data of imitation learning, and inputs it to the data and communication module.

[0042] Because different sensor models differ in timestamp accuracy, topic / message format, coordinate system, and calibration method, existing systems often directly interface with the original topic at the ROS / ROS2 layer, which means that the upper-layer preprocessing and status recognition nodes must be customized according to the model. Once the sensor or robotic arm interface is changed, the data pipeline and algorithm need to be rewritten or significantly modified, resulting in high adaptation costs, poor reusability, and long deployment cycles.

[0043] Meanwhile, since the acquisition side and the reasoning / planning side mostly use raw or semi-structured data for point-to-point communication, there is a lack of unified data semantic specifications and standardized structures. This makes it difficult to directly reuse the observation data modules when crossing robotic arms and projects. System evolution requires repeated adaptation, resulting in high module coupling and low reuse rate.

[0044] Therefore, the data and communication module in this embodiment, based on the ROS2 bus's topic / service mechanism, inserts a data translator node between the sensor's native topics and the upper-layer algorithm. This transforms different types of messages from different sensor models into a unified dictionary structure and transmits them in JSON format. By setting up a sensor data translator on top of ROS2, heterogeneous topics are unified and standardized, allowing for minimal adaptation when adding or replacing sensors / robotic arms without affecting the upper-layer algorithm logic. The data and communication module uniformly connects to heterogeneous sensors and robotic arms, providing standardized observation semantics and decoupling the system from hardware models. Adding or replacing hardware only requires modifying the decoder plugin, without affecting the upper-layer algorithm.

[0045] In existing solutions, model inference platforms in the field of robot control often rely on specific model training platforms, with their interfaces and data formats tightly bound. When it's necessary to change the training platform (such as switching to a higher-performance distributed training framework), extensive adaptation and testing of the inference platform is required. This leads to: High migration costs: Replacing the training platform requires refactoring some inference platform code; Limited technology updates: In order to avoid high migration costs, the system is unable to introduce new training platform technologies in a timely manner; Cross-platform collaboration is difficult: When different research teams share models, platform compatibility issues can increase the workload.

[0046] Because data formats, model export methods, and operator / driver versions are interdependent between the training and inference ends, changing the training framework or hardware platform requires extensive adaptation and integration on the inference side. This leads to high costs for platform migration, version upgrades, and maintenance, and extends the technology update cycle. Therefore, there is an urgent need for a modular inference platform design that allows communication between the platform and the training platform via standardized interfaces, thereby reducing the costs of platform switching and upgrades.

[0047] In this embodiment, the model bridge and intermediate representation module isolate the runtime environment of the model inference platform from the training platform environment via sockets, and connects to different training platforms through different model bridge classes. Since the inference environment and training platform environment are isolated via sockets, there is no need to worry about conflicts between them.

[0048] The model bridge and intermediate representation module send observation data and receive inference results output by the model based on socket technology, and transmit data with the training platform through the model bridge class.

[0049] By using the Model Bridge module to manage connections to different training platforms, this system gains the ability to interface with various training platforms. Within the Model Bridge subclasses, each Model Bridge initializes models for its respective platform and processes observation data and model output according to model requirements. Furthermore, the Model Bridge isolates the inference environment from the training platform environment via sockets, resulting in greater compatibility.

[0050] In the field of action generation through robot imitation learning, existing technologies often use diffusion models based on the UNet architecture as generators, but their generalization ability and action understanding ability in complex tasks are limited. Poor task adaptability: It lacks the ability to migrate across tasks and scenarios and requires a large amount of task-specific data; Limited depth of action understanding: It struggles to capture complex temporal dependencies in high-dimensional, multimodal input data; Insufficient diversity of generated actions: It is prone to pattern collapse, resulting in simple and inflexible generated actions; Because UNet primarily uses spatial convolutions, its ability to fuse and represent long temporal dependencies and multimodal data (such as multi-view video and robotic arm states) is limited. This results in insufficient generalization ability across tasks and scenarios, and a strong dependence on task-specific data volumes, thereby increasing data and annotation costs and reducing transfer efficiency. Therefore, a more advanced model architecture, such as EquiDiff combined with Transformer, is needed to improve generalization and learning capabilities across multiple tasks and scenarios.

[0051] The learning and inference module of this embodiment integrates the EquiDiff equivariant diffusion model and Transformer temporal modeling, uses group convolution to replace standard convolution, introduces symmetry, equivariant priors and sequence modeling capabilities, and integrates multimodal observations and task conditions under the equivariant constraints of the three-dimensional Euclidean group to enhance the learning of long-term and multimodal information and reduce the dependence on task-specific data.

[0052] In existing technologies, teaching data often directly records the controller's raw output without uniform trajectory shaping (such as resampling, filtering, and jerk constraints). Training samples are prone to noise and unbalanced temporal sampling, which leads to motion jitter and large tracking errors during inference and execution. This makes it difficult to meet the industrial requirements for smooth and consistent motion in factory logistics and precision assembly.

[0053] The teaching and data quality module in this embodiment constructs a high-quality teaching and trajectory monitoring process, performs timestamp quality monitoring on the teaching trajectory, and calculates whether the mean and variance of the timestamp frequencies of each sensor collected are within the threshold range.

[0054] The trajectory shaping in the teaching and data quality module specifically includes: resampling the teaching trajectory at fixed periods and low-pass filtering; constraining velocity / acceleration / jerk; smoothing the data with splines; and performing jitter and hysteresis processing on the gripper curve.

[0055] The quality monitoring of the teaching and data quality module includes: thresholding the mean and variance of timestamp frequencies for each modality, monitoring the frame loss rate and cross-modal timing deviation, marking or removing unqualified samples, and generating a quality report.

[0056] The robotic arm in this implementation is a dexterous main arm constructed from a servo motor or a servo motor with an encoder. The dexterous main arm has a sampling frequency of ≥200 Hz and an angular resolution of ≤0.1°.

[0057] This embodiment uses low-cost servo motors (≥200Hz, ≤0.1°) to acquire high-resolution teaching trajectories, and performs resampling, low-pass filtering, velocity / acceleration / jerk limiting, and spline smoothing; it also performs threshold detection and reporting on the mean / variance of multimodal timestamp frequencies, frame loss rate, and cross-modal time difference to ensure industrial-grade smoothness and consistency.

[0058] This embodiment introduces a data translator and unified observation semantics on ROS2, decoupling the upper-layer algorithm from direct coupling with specific sensor / robotic arm models. This allows for hardware replacement or addition with minimal adaptation decoder plugins, significantly reducing adaptation costs and deployment cycles, and improving cross-device reusability. Furthermore, this embodiment employs a Hardware Abstraction Layer (HAL) to handle control issuance and status readback, shielding differences between different robotic arms / endpoints and forming a standardized production line-level control interface. Within the closed loop of dexterous main arm teaching + trajectory shaping + quality monitoring, the timestamp stability and trajectory smoothness of the training data are guaranteed, suppressing noise and jitter in the teacher's movements. This allows for more thorough model learning and smoother online execution, meeting the consistency and repeatability requirements of industrial scenarios.

[0059] On the learning and deployment side, a temporal generation architecture of EquiDiff + Transformer is adopted, and multimodal observations are fused with task conditionalization and SE(3) equivariance constraints to enhance the ability to express long temporal dependencies and cross-scene changes. Better generalization performance and action accuracy can be obtained with less task-specific data. The training platform and inference environment are decoupled by model bridge + intermediate representation (I / O contract), so that the inference end does not need to be reconstructed when changing / upgrading the training framework, thereby reducing the cost of platform migration and version upgrade. Combined with real-time inference runtime and a complete safety chain (amplitude limiting, collision detection, emergency stop, graded rollback), the anomalies are controlled and traceable while meeting the control cycle, achieving industrial-grade stability and security. In summary, this embodiment forms a causal closed loop in five dimensions of "hardware decoupling, data quality, model generalization, training-inference decoupling and industrial reliability" to address the shortcomings of the existing technology, thereby improving the overall R&D efficiency and implementation effect.

Claims

1. A training and control system for an embodied intelligent universal robot, characterized in that, It includes sensors, embodied intelligent actuators, and decoupled model inference and training platforms: the data and communication module connects to the sensors and embodied intelligent actuators, unifies heterogeneous messages from different types of sensors and embodied intelligent actuators, and decouples the robot from the environmental data monitored by the sensors; the model bridge and intermediate representation module isolate the inference platform from the training platform, receiving the unified observation data from the data and communication module; the model inference platform has a learning and inference module, which uses EquiDiff combined with Transformer for temporal modeling; it also has a teaching and data quality module, which performs trajectory shaping and quality monitoring on the teaching action data of imitation learning, and inputs it to the data and communication module.

2. The embodied intelligent universal robot training and control system according to claim 1, characterized in that, The data and communication module is based on the topic / service mechanism of the ROS2 bus. It inserts a data translator node between the sensor's native topics and the upper-layer algorithm, transforming different types of messages from different sensor models into a unified dictionary structure and transmitting them in JSON format.

3. The embodied intelligent universal robot training and control system according to claim 1, characterized in that, The model bridge and intermediate representation module isolate the runtime environment of the model inference platform from the training platform environment through a socket, and connect to different training platforms through different model bridge classes.

4. The embodied intelligent universal robot training and control system according to claim 1 or 3, characterized in that, The model bridge and intermediate representation module send observation data and receive inference results output by the model based on socket technology, and transmit data with the training platform through the model bridge class.

5. The embodied intelligent universal robot training and control system according to claim 1, characterized in that, The learning and reasoning module integrates the EquiDiff equivariant diffusion model with Transformer temporal modeling, uses group convolution to replace standard convolution, introduces symmetry, equivariant priors and sequence modeling capabilities, and integrates multimodal observations and task conditions under the equivariant constraints of the three-dimensional Euclidean group to enhance the learning of long-term and multimodal information and reduce the dependence on task-specific data.

6. The embodied intelligent universal robot training and control system according to claim 1, characterized in that, The teaching and data quality module constructs a high-quality teaching and trajectory monitoring process, performs timestamp quality monitoring on the teaching trajectory, and calculates whether the mean and variance of the timestamp frequencies of each sensor collected are within the threshold range.

7. The embodied intelligent universal robot training and control system according to claim 1 or 6, characterized in that, The trajectory shaping in the teaching and data quality module specifically includes: resampling the teaching trajectory at fixed periods and low-pass filtering; constraining velocity / acceleration / jerk; smoothing the data with splines; and performing jitter and hysteresis processing on the gripper curve.

8. The embodied intelligent universal robot training and control system according to claim 1 or 6, characterized in that, The quality monitoring of the teaching and data quality module includes: thresholding the mean and variance of timestamp frequencies for each modality, monitoring the frame loss rate and cross-modal timing deviation, marking or removing unqualified samples, and generating a quality report.

9. The embodied intelligent universal robot training and control system according to claim 1, characterized in that, The embodied intelligent actuator is constructed from a robotic arm using servo motors or servo motors with encoders.

10. The embodied intelligent universal robot training and control system according to claim 9, characterized in that, The robotic arm with an embodied intelligent actuator has a sampling frequency of ≥200 Hz and an angular resolution of ≤0.1°.

Images