Humanoid robot Unitree G1 Edu Flagship E-U7

Unitree G1 Edu Flagship E-U7 Humanoid Robot for AI Research and Development

Unitree G1 Edu Flagship E-U7 is a humanoid robot designed for scientific research, education, artificial intelligence algorithm development, and human-robot interaction experiments, featuring 41 degrees of freedom, a computational NVIDIA Jetson Orin NX 16GB module, and two Revo 2 Basic five-fingered hands. The model supports secondary development and works with ROS2 and SDK2 tools, allowing for real-time programming of movements, navigation, and object interaction. Equipped with an Intel RealSense D435i camera, 3D LiDAR, each hand can hold and carry loads up to 3 kg.

Advantages and Features of Unitree G1 Edu Flagship E-U7

• Degree of freedom and humanoid kinematics. The Unitree G1 Edu Flagship E-U7 humanoid robot has 41 degrees of freedom and supports expansion to 43 in the maximum configuration. Large joint movement angles of the arms, legs, and body ensure complex manipulations, balancing, and movement in research project conditions.
• Humanoid Revo 2 Basic hands. The flagship version is equipped with two humanoid hands with position and force control support. Manipulators can work with fragile objects and lift loads weighing up to 3 kg.
• High computational power. The onboard NVIDIA Jetson Orin NX 16GB module over 100 TOPS is optimized for computer vision processing, simulation learning, and reinforcement learning methods. Works with multimodal AI models UnifoLM.
• Comprehensive spatial perception. 360° circular view is provided by the proprietary Unitree 4D LiDAR-L1, Intel RealSense D435i depth camera, and additional 3D LiDAR for navigation and 3D SLAM. An array of 4 microphones is responsible for command recognition. This allows building three-dimensional room maps, recognizing obstacles, and working with voice commands.
• Flexible programming. Full low- and high-level control over drives through unitree_sdk2 (C++/Python) is open. The robot is fully compatible with ROS2 and has ready-made URDF digital models for simulation in Gazebo and Isaac Sim, simplifying algorithm testing before launching on real equipment.
• Reliable power mechanics. Knee drives develop torque up to 120 Nm, and industrial roller bearings are designed for long-term operation. A removable 9000 mAh battery provides up to 2 hours of autonomous operation, and the foldable body 690x450x300 mm facilitates transportation between laboratories and educational centers.
• Official extended warranty. The Edu series robotic complex comes with an official 18-month warranty, providing full technical support and service.

Design and Hardware

The robotic platform has a stable architecture with a height of 1320 mm and a total weight of about 35 kg, designed for long experiments and intensive dynamic loads. The knee joints are equipped with high-efficiency permanent magnet motors (PMSM) with low inertia, generating torque up to 120 Nm for instant body position stabilization, running, or overcoming obstacles.

Large deviation angles of the pelvic, knee, and waist joints allow for the implementation of complex movement scenarios unavailable to most other humanoid platforms. All moving joints of the mechanism are equipped with industrial cross-roller bearings for high positioning accuracy. Cable wiring is laid hidden inside the hollow frame elements, fully protecting communication lines from mechanical wear. An important engineering advantage is the presence of a built-in 5W speaker in the head, which, together with an array of microphones, provides full two-way voice communication. For convenient transportation between laboratories or classrooms, the body folds to compact dimensions — 690×450×300 mm.

Precise Object Handling with Robotic Hands

The flagship configuration of the Unitree G1 Edu Flagship E-U7 is equipped with two full-fledged humanoid manipulators with 5 fingers on each hand, whose design is close to the kinematics of the human body. The main feature of the Revo 2 Basic hands is the integrated hybrid force and position control of the joints, allowing the device to accurately calculate the force of interaction with the environment. This allows the robot to delicately work with objects of various geometric shapes, densities, and the fragility of glassware and tools.

The anatomical structure of the manipulators supports the collection of data on pressing force and material resistance in real-time, helping researchers to work out precise capture scenarios. Each hand's lifting capacity is up to 3 kg, fully covering the needs of most research, logistics, and educational tasks involving object movement.

Control Systems and Computational Resources

For engineers and developers, the Unitree G1 Edu Flagship E-U7 offers full low-level and high-level control over drives and sensors through the open unitree_sdk2 interface in C++ and Python programming languages. Full integration with the ROS2 operating system simplifies the connection of third-party software modules and plugins. The presence of detailed URDF digital models allows for accurate simulation of the humanoid's physical behavior in virtual environments Gazebo and NVIDIA Isaac Sim before testing algorithms on real hardware.

Standardized holes are provided on the robot's back for secure attachment of additional equipment. For connecting third-party sensors and payload, power lines of various voltages at 12V, 24V, and 58V are output, as well as high-speed engineering interfaces: Gigabit Ethernet RJ45 ports, USB-C, and industrial buses GPIO, I²C, and UART.

Sensors for Navigation and Object Recognition

Spatial orientation and map building in three-dimensional space are based on a complex combination of modern machine vision sensors. The robot's head integrates a proprietary circular Unitree 4D LiDAR-L1, performing 21,600 laser scans per second, providing ultra-precise scanning of the surrounding environment without blind spots.

The perception system is complemented by an Intel RealSense D435i depth RGB-D camera and an additional long-range 3D LiDAR. This hybrid sensor array allows the robot to build 3D maps using SLAM technology without delays, recognize objects on the path, and dynamically avoid obstacles. An array of 4 built-in microphones is responsible for receiving and analyzing acoustic information, implementing algorithms for precise localization of the sound source in the room and recognizing user voice commands.

Platform for Machine Learning and Autonomous Systems

The main computational node of the Unitree G1 Edu Flagship E-U7 is an onboard computer based on the NVIDIA Jetson Orin NX module with a capacity of 16 GB. With computational power exceeding 100 TOPS, the platform is ideally optimized for local processing of heavy neural networks and computer vision algorithms directly on the robot's board.

The model supports advanced machine learning methods — simulation learning (based on human operator actions) and reinforcement training (Reinforcement Learning). Software capabilities and movement patterns are regularly updated via wireless OTA packages. The hardware is fully adapted for local work with large multimodal artificial intelligence models UnifoLM. This allows programming completely autonomous robot behavior, recognizing complex sequential engineering commands, and interacting with people without the need for constant connection to cloud servers.

Application Areas

Unitree G1 Edu Flagship E-U7 is created for scientific and educational projects. The platform allows conducting experiments with autonomous mobility, low-level drive control, and manipulator management in the following areas:

• Scientific research and R&D: development of artificial intelligence algorithms, testing of reinforcement learning methods (Reinforcement Learning), and modeling of human-machine interaction.
• Navigation and spatial perception: research of autonomous navigation, computer vision, building three-dimensional room maps, and SLAM algorithms.
• Manipulation and motion control: practicing delicate object capture, testing walking patterns, and dynamic motion control algorithms.
• Education and applied application: teaching students robotics and mechatronics, conducting university laboratory projects, and designing service robots.

Compatibility with Educational Platforms

Unitree G1 Edu Flagship E-U7 is adapted for use in universities, research laboratories, and educational centers. The open architecture simplifies integration with additional equipment and software complexes.

The humanoid robot supports work with:

• ROS 2;
• Python;
• C++;
• NVIDIA JetPack;
• external cameras;
• additional LiDAR sensors;
• laboratory data collection systems;
• research machine learning platforms;
• network Ethernet devices;
• GPIO modules and peripheral equipment.

Thanks to standardized connection interfaces, the platform easily adapts to various scientific and educational projects. The availability of technical documentation and developer tools allows quickly starting work on your own research tasks.

Unitree G1 Edu Flagship E-U7 Package

The device comes assembled and ready for initial launch after unpacking. The basic package includes all necessary elements for autonomous power and wireless control.

1. Unitree G1 Edu Flagship E-U7 humanoid robot.
2. Quick-release lithium battery with a capacity of 9000 mAh.
3. Network charger with parameters 54 V, 5 A.
4. Wireless handheld control remote.
5. User manual and technical passport.

Technical Specifications of Unitree G1 EDU

Usage Tips

• Power control. Always check the battery charge level before each launch to avoid sudden shutdowns during movements.
• Surface preparation. Use only flat, dry, and non-slip surfaces for practicing walking, stabilization, and balancing algorithms.
• System updates. Regularly update software and motion skill packages through official wireless tools (OTA) from Unitree.
• Development ecosystem. For creating your own algorithms, simulations, and secondary development, it is recommended to use official ROS2 and unitree_sdk2 environments.

Warnings and Safety

• Launch without support. Do not power on the Unitree G1 Edu when it is lying on the ground or in an unstable position without support. Initial launch and calibration should only occur on a protective hanging stand or special chair.
• Rough battery installation. Avoid forcing the battery into the fuselage with excessive physical force. If the block does not smoothly fit into the slots until it clicks, immediately stop the process to avoid damaging internal power connectors.
• Programming without Debug mode. Do not send custom commands through the SDK without first switching the robot to development mode (L2 + R2). Ignoring this rule causes a hard conflict with the built-in motion controller, leading to severe jitter and drive failure.
• Overloading. Do not load the Revo 2 Basic robotic hands with more than 3 kg. Overloading will lead to critical overheating of servos and failure of gearboxes.
• Hot sensor connection. Do not connect or disconnect third-party sensors and expansion modules to GPIO, I²C, UART interfaces on the robot's back when the system is on and voltage is applied to the ports. All manipulations with the payload are performed only on a de-energized device.

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