The Inertial Measurement Unit (IMU), which includes accelerometers, gyroscopes, and magnetometers, can real-time collect acceleration and angular velocity data of joint movements with a refresh rate of over 1000Hz. It provides dynamic motion parameters for wrist position estimation and assists visual data in completing spatial coordinate calibration. The encoder installed in the robot's servo motor calculates joint displacement by recording the motor's rotation angle, with an accuracy of less than 0.1°, which can accurately track the motion trajectory of the wrist and arm, providing precise coordinate reference of limb movement for perspective conversion. In some high-end applications, a VSLAM (Visual Simultaneous Localization and Mapping) module is also integrated, which realizes the robot's self-localization and environmental modeling through a self-developed SLAM algorithm, supports real-time loop closure detection and spatial anchor-assisted localization, and further improves the stability of wrist position estimation in complex environments.
Wrist World technology needs to process massive amounts of visual and sensor data, which puts strict requirements on computing speed and data transmission efficiency. The edge computing unit adopts a high-performance SOC (System on Chip), integrates an independent CNN (Convolutional Neural Network) engine, and can realize localized real-time data processing to avoid delay problems caused by cloud transmission. This unit supports multi-threaded parallel computing, which can process multiple video streams and sensor data simultaneously, complete image preprocessing, feature extraction, and perspective conversion operations, ensuring that the delay in generating the first-person image is controlled at the millisecond level. The high-speed communication interface connects each component to the computing unit through USB Type-C or Ethernet interface connectors, supporting a data transmission rate of not less than 10Gbps, and is compatible with the ROS/ROS2 development framework to ensure efficient transmission of visual data, sensor data, and computing instructions. Some solutions will integrate industrial Ethernet connectors to further improve anti-interference ability and better adapt to industrial-grade operation scenarios.
The precise operation of the robot needs to be completed by executive components, and the operation effect depends on the verification of feedback components. As the core of joint drive, the servo motor has high-precision position control capability with a response time of less than 5ms. Combined with the encoder, it can realize precise movement of the wrist and arm, ensuring the accuracy of action execution under the guidance of the first-person perspective. The tactile sensor installed at the end of the robot's hand can collect tactile data such as grasping force and contact pressure, which not only can feedback the operation effect and assist self-supervised learning to optimize the action strategy, but also can provide tactile-visual correlation data of the operation scene for perspective conversion.
The wire harness is divided into power wire harness and signal wire harness. The power wire harness provides high-current power support for the servo motor to ensure stable power under high-intensity work. The signal wire harness accurately transmits the feedback data of tactile sensors and encoders, with transmission delay controlled at the microsecond level, providing efficiency guarantee for the closed-loop calibration of self-supervised learning. Considering the high-frequency movement characteristics of the robot's wrist joint, the connectors and wire harnesses need to have extremely strong flexibility and fatigue resistance, and meet the requirements of miniaturization and anti-interference design to avoid poor contact during movement. Its performance directly affects the control effect of the perspective conversion error of Wrist World technology. A high-quality connection system can help reduce the perspective conversion error by more than 42.4%, becoming a key support for realizing precise operations.
Key Software System - The Core Brain of Data Processing and Intelligent Decision-Making
If the hardware components are the bones and muscles of Wrist World technology, then the software system is its core brain, which completes data processing and intelligent decision-making through multi-module collaboration, realizing core functions such as perspective conversion, self-supervised learning, and spatial reconstruction.
As the central platform for information integration, the multi-modal data fusion system is responsible for receiving multi-source data such as vision, positioning, and posture, eliminating errors of different devices through data calibration and fusion algorithms, and forming a unified data set. In terms of data synchronization and calibration, the system adopts timestamp synchronization technology to unify the data streams of cameras, IMU, encoders and other devices on the same time axis, avoiding spatial coordinate deviations caused by data delay. At the same time, it corrects camera distortion and sensor errors through calibration algorithms to ensure the consistency of all data. In the multi-source data fusion link, algorithms such as Kalman filtering and Bayesian estimation are used to fuse the environmental information of visual data, the motion data of IMU, and the position data of encoder into a unified spatial state vector, providing comprehensive and accurate input data for the 4D world model.
The 4D world model system is the core of Wrist World technology. It constructs a dynamic and interactive scene model by fusing 3D spatial data and time dimension information, which is also the core logic for realizing perspective conversion. In terms of 3D spatial reconstruction, the system constructs a 3D model of the operation scene through point cloud stitching and mesh reconstruction algorithms based on the depth data of binocular cameras and TOF sensors, accurately restoring the environmental layout, the 3D shape of the operation target, and the spatial positional relationship of the robot's limbs. Time dimension modeling integrates the motion data of IMU and encoder to record the time series changes of robot actions, constructs a dynamic motion trajectory model, captures the complete movement process of the wrist from the initial position to the operation position, and provides time-dimensional action continuity support for perspective conversion. As a key link, the perspective conversion algorithm extracts and generates the first-person operation image from the wrist perspective from the third-person global image by using perspective transformation and view synthesis algorithms based on the 3D spatial model and motion trajectory data. By learning the visual rules of human perspective conversion, the algorithm can automatically compensate for the information of occluded areas, ensuring the integrity and accuracy of the first-person image.
The self-supervised learning system is the key for Wrist World technology to get rid of the dependence on manual annotation. It realizes the autonomous optimization of wrist position estimation and perspective conversion accuracy through built-in algorithms. In the unsupervised feature extraction link, the system uses deep learning models such as Convolutional Neural Network (CNN) and Transformer model to automatically extract key features from a large number of unlabeled third-person images, including wrist contour, operation target features, environmental texture information, etc., and builds a complete feature database. The closed-loop calibration mechanism compares the generated first-person image with the real image collected by the wrist local camera, calculates the perspective conversion error, adjusts the parameters of the 4D world model through backpropagation algorithm, and optimizes the perspective conversion accuracy. At the same time, combined with the feedback data of the tactile sensor, it verifies the effectiveness of the operation action, independently corrects the wrist position estimation model, and forms a closed-loop learning of data collection - model optimization - accuracy improvement. In addition, the transfer learning module can transfer the learned perspective conversion ability to new operation scenarios, quickly adjust model parameters through a small amount of scene adaptation data, improve the scene adaptability of the technology, and reduce the training cost in new scenarios.
The real-time control system undertakes the responsibility of the scheduling center for command execution, which combines the first-person image after perspective conversion with operation commands to drive the robot to complete precise actions. In terms of motion planning, the system plans the motion path of the wrist and hand based on the target position and environmental information in the first-person image, avoids collision risks, and ensures the fluency and accuracy of operation actions. In the command issuance and feedback link, the system converts the planned action commands into control signals of the servo motor and issues them in real time, and at the same time receives the feedback data of the encoder and tactile sensor, dynamically adjusts the action parameters, and ensures that the operation accuracy meets the expected requirements.
Cross-Module Linkage Mechanism - Coordinated Technical Operation
The efficient operation of Wrist World technology ultimately relies on the seamless collaboration between hardware components and software systems, forming a full-process linkage mechanism of collection - processing - modeling - conversion - execution - optimization.
In the data collection stage, external panoramic cameras and depth perception devices synchronously collect third-person global images and 3D spatial data. IMU and encoders real-time capture the motion state and position information of the robot's wrist. These multi-source data are transmitted to the edge computing unit through high-speed communication interfaces, and the multi-modal data fusion system completes synchronous calibration and integration to provide a unified and accurate data set for subsequent processing.
In the modeling and conversion stage, the integrated data set is input into the 4D world model system to construct a dynamic scene model including spatial and time dimensions. Based on this model, the perspective conversion algorithm generates a first-person operation image combined with wrist motion trajectory data, and at the same time performs preliminary calibration through the real image collected by the wrist local camera to ensure the accuracy of the image.
In the learning and optimization stage, the self-supervised learning system compares the differences between the generated first-person image and the real image, combines the operation feedback of the tactile sensor, independently optimizes the perspective conversion algorithm and the wrist position estimation model, and continuously improves the data accuracy. The entire process does not require manual annotation intervention, realizing autonomous iterative upgrading.
Finally, in the execution and feedback stage, the real-time control system plans precise operation commands based on the optimized first-person image and issues them to the servo motor to drive the robot to complete actions such as grasping and flipping. During the execution process, each sensor continuously collects data and dynamically adjusts the action parameters to ensure that the operation effect meets the expected requirements.
The core advantage of this linkage mechanism is to realize the closed-loop operation of data collection - intelligent processing - autonomous optimization - precise execution. It not only fundamentally solves the bottleneck of scarce first-person data, but also continuously improves the system accuracy through self-supervised learning, providing reliable technical support for robots in fields with extremely high requirements for operation accuracy such as precision manufacturing and medical surgery, and also opening up a new direction for the development of robot spatial perception technology.