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| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| Dec 29, 2023 | IPO | $125M | $4.8B | Hong Kong Stock Exchange listing |
Founded UBTECH Robotics in 2012 in Shenzhen. The company built the Walker series of humanoid robots, deployed today in industrial and service settings, and developed a large AI-education business serving schools across China. UBTECH listed on the Hong Kong Stock Exchange in December 2023, the first humanoid robotics company to do so. Zhou Jian holds a degree in computer science and had prior experience founding technology ventures before UBTECH.
Full-body bipedal industrial humanoid with 52-DOF kinematic architecture and fourth-generation dexterous hands capable of precise manipulation tasks including gripping, assembly, inspection, and material handling. Distinctive for its autonomous hot-swappable battery swap system, enabling autonomous battery swaps within 3 minutes using dual-battery dynamic balancing and dual-arm coordinated precision technologies. First industrial humanoid integrated with UBTECH's Co-Agent intelligent agent system. Began mass production and delivery in November 2025, targeting 500 units within that year. Already deployed in automotive manufacturing, smart factories, intelligent logistics, and data collection centers.
Industrial humanoid robot standing 1.72 meters tall with a 41-DOF architecture for industrial stability and dexterity. Features integrated perception and control with end-to-end learning-based whole-body motion control for dexterous manipulation and stable walking in complex non-structured tasks. Equipped with advanced planning model technology for task intention understanding and planning. Specifically engineered to solve three key industrial challenges: visual positioning under varying lighting, motion control under high-load conditions, and joint cooling during long-term operation. A full fleet commenced operational deployment at Zeekr's 5G smart factory in Ningbo in 2025, and has doubled efficiency in parts assembly at BYD's electric vehicle plants with mass deployment slated for Q2 2025.
Full-size wheeled humanoid robot standing 176 cm tall with 44 degrees of freedom, positioned between UBTECH's smaller service-focused Cruzr line and the bipedal Walker industrial humanoids. Integrates gen-4 industrial dexterous hands achieving sub-millimeter precision manipulation and handles 15 kg payload across 0-1.8 m workspace. Self-developed passive binocular vision system employs deep learning stereo depth estimation to enable human-eye stereoscopic perception. Moves at up to 2 m/s with symmetrical body design permitting ±170° rotation in 0.8 m passages, targeting factory sorting, assembly, and tight-space logistics.
Industrial-grade autonomous mobile robot (AMR) engineered for indoor logistics and service, featuring multi-scenario delivery for restaurants, supermarkets, retails, hotels, airports and factories. Its open-tray design allows for easy loading and unloading, while laser SLAM and AI visual technology achieve high-precision positioning in complex environments. Features include 360° surround microphone array, voice recognition, eye tracking, intelligent tray with sensors, independent obstacle crossing wheels, and a 21 inch UHD display for digital signage. The robot gets 8 hours of use on a full charge with its lithium iron phosphate battery. Deployed at Switzerland's Gfellen Mountain Restaurant in 2023.
Portable humanoid robot standing just under 10 inches tall , designed for classroom and home education. The Alpha Mini engages through voice, facial expressions, body gestures, and is empowered by 4G LTE, face and object recognition . Its 14 servo motors enable dynamic actions like dancing and kung fu, while the charming LCD eyes exhibit emotions . It supports graphical programming with multi-conditional modules of vision/sensing/speech , combining Blockly, Scratch, Python and JavaScript programming languages for curriculum integration. One of Alpha Mini's key missions is to boost robotics education .
Open-source AI-enabled humanoid robot platform and modular kit series for K–12 education. Yanshee stands nearly 15 inches tall, combining a Raspberry Pi-based processor with 17 servo-driven joints, an 8 MP camera, dual speakers and microphone, and extensible sensors for high school and university STEM programs. It can perform tasks on command, test environmental conditions, speak, listen, answer questions, and relay data. Yanshee supports Java, C/C++, Python, Perl, and Blockly programming. UGOT is a multi-mimetic AI educational kit that can be assembled into 7+ distinct robotic forms—quadrupeds, self-balancing vehicles, robotic arms, and walkers. Powered by a 1 TOPS NPU AI processor, UGOT includes 720p RGB camera, voice and gesture recognition, object detection, and mobile app control. In 2024, UGOT won the IF Gold Award and the Red Dot Best of the Best Award.
Abstract A method for controlling a robotic arm that includes an end effector and a sensor that are mounted at an end of the robotic arm includes: obtaining, by the sensor, n gravity matrix data, wherein the n gravity matrix data are gravity matrix data of the end effector in an end coordinate system when the robotic arm is in a different poses, n≤3; determining n rotation transformation matrices from a base coordinate system of the robotic arm to the end coordinate system when the robotic arm is in n different poses; calculating coordinates of a center of mass and mass of the end effector based on the n gravity matrix data and the a rotation transformation matrices; and controlling the robotic arm based on the coordinates of the center of mass and the mass.
Abstract A mapping method, a computer-readable storage medium, and a robot are provided. The method is applied to a robot including a first lidar and a second lidar, where the first lidar is installed at a position higher than that of the second lidar. The method includes: obtaining a first laser key frame; calculating a current pose of a robot based on the first laser key frame; updating a first probability map based on the current pose and the first laser key frame; obtaining a second laser key frame; updating a second probability map based on the current pose and the second laser key frame; and generating a fused grid map for navigating the robot based on the updated first probability map and the updated second probability map, thereby greatly improving the stability of positioning and navigation.
Abstract A robot control method includes: building a two-wheeled inverted pendulum model based on a wheel-legged robot; constructing initial state-space equations based on the two-wheeled inverted pendulum model; linearizing the initial state-space equations to obtain the state-space equations for a linear time-invariant system; obtaining a quadratic performance objective function according to the state-space equations for the linear time-invariant system; and solving the quadratic performance objective function by a linear quadratic regulator to obtain wheel torques of the wheel-legged robot, and controlling the wheel-legged robot according to the wheel torques.
Abstract A swimming pool cleaning system includes: a pool cleaning robot (10) that is to at least clean a floor and pool walls of a swimming pool; and a surface cleaning device (20) that is to at least clean a pool surface of the swimming pool. The surface cleaning device (20) is wirelessly connectable to an external control device and has a connecting cable (21) that electrically connects the surface cleaning device (20) to the pool cleaning robot (10), so that the pool cleaning robot (10) communicates with the external control device via the surface cleaning device (20).
Abstract A robotic arm angle interval inverse solving method and a robotic arm using the same are provided. The method includes: obtaining a joint angle calculation model and a differential relationship model of a target joint of the robotic arm; obtaining extreme arm angles corresponding to a joint angle of the differential relationship model at extreme values based on the differential relationship model; obtaining a joint arm angle interval corresponding to the target joint based on the extreme arm angle and the joint angle calculation model; and obtaining a target arm angle interval corresponding to the robotic arm based on the joint arm angle interval corresponding to the target joint of the robotic arm. In comparison with the existing method to solve the arm angle interval of the robotic arm, a more accurate arm angle interval can be obtained.
Abstract A robot calibration method, a robot, and a computer-readable storage medium are provided. The method includes: obtaining operation space information of the execution end of the robot; obtaining operation space points after gridding an operation space of the robot by gridding the operation space based on the operation space information; obtaining calibration data by controlling the execution end to move to the operation space points meeting a preset requirement; and calibrating the hand and the image detection device of the robot based on the obtained calibration data. In this manner, the operation space points are determined by gridding the operation space based on the operation space information, and the execution end can be automatically controlled to move to the operation space points that meet the preset requirements so as to obtain the calibration data in an automatic and accurate manner, thereby simplifying the calibration process and improving the efficiency.
Abstract A robot joint configuration determining method, a robot using the same, and a computer readable storage medium are provided. The method includes: simulating a joint model of a first joint of the robot using first motion deviation data to obtain first result data; simulating the joint model using second motion deviation data to obtain second result data; taking the motion deviation data corresponding to one of the first result data and the second result data meeting one or more preset conditions as a target motion deviation data for the first joint; and determining type information of a reducer in a configuration information of the first joint based on the target motion deviation data. In the present disclosure, the motion deviation of the first joint that is relatively accurate can be obtained through the results of the two simulations.
Abstract A person re-identification method, a storage medium, and a terminal device are provided. In the method, a loss function used during model training is a preset distribution-based triplet loss function constraining a difference between a mean of a negative sample feature distance and a mean of a positive sample feature distance to be larger than a preset difference threshold; where the positive sample feature distance is a distance between a feature of a reference image, and a feature of a positive sample image, and the negative sample feature distance is a distance between the feature of the reference image and a feature of a negative sample image. In this manner, it can constrain the mean of the positive sample feature distance and that of the negative sample feature distance, thereby improving the accuracy of person re-identification results.
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