Top 10 Yahboom Robotics Kits to Watch in 2026— A Complete Guide with Tutorials and Videos

①ROSMASTER M3 Pro ROS2 Robot

 Overview:

ROSMASTER M3 Pro is a highly integrated embodied intelligent robot platform developed by Yahboom specifically for ROS education, scientific research experiments, and AI application teaching. It utilizes Mecanum wheels and pendulum suspension chassis for omnidirectional movement. Developed based on the ROS2 Humble system, equipped with a 6DOF robotic arm and a binocular structured light depth camera to perform tasks such as visual recognition, 3D grasping, and precise handling. With dual TOF LiDAR, it enables stable and reliable SLAM mapping and autonomous navigation, as well as LiDAR obstacle avoidance and path planning. Unlike traditional ROS robots, the ROSMASTER M3 Pro deeply integrates cutting-edge AI large-scale model technology. Built-in speech recognition and natural language understanding modules, can realize voice command control, multimodal interaction with text/image/voice, task planning and execution, and dynamic environment perception. Whether used in AI courses, robotics algorithm teaching, or university research projects, the ROSMASTER M3 Pro provides a stable, powerful, and easily scalable experimental platform, making it an ideal choice for AI and robotics education.

🔗 Tutorial:

https://www.yahboom.net/study/ROSMASTER-M3PRO

🎥 Video:

②ROSMASTER X3 ROS2 AI Voice Interaction Robot

 Overview:
ROSMASTER X3 is an educational AI voice interaction robot based on the robot operating system with Mecanum Wheel, compatible with Jetson NANO/Orin NX SUPER/Orin NANO SUPER and Raspberry Pi 5. It is equipped with lidar, depth camera, voice interaction module and other high-performance hardware modules. Using Python programming, ROSMASTER X3 can realize mapping and navigation, following or avoiding, Autopilot and human body posture detection. It support APP remote control, APP mapping navigation, handle remote control, ROS2 system PC control and other cross-platform remote control methods. We provide 103 video courses and a large number of codes, which can allow users to learn artificial intelligence programming and ROS systems.

🔗 Tutorial:

https://www.yahboom.net/study/ROSMASTER-X3

🎥 Video:

③Raspbot V2 AI Large Model ROS2 Robot

 Overview:

RASPBOT-V2 is an AI larger model robot car with a metal body bracket. It is equipped with Mecanum wheels to achieve 360° omnidirectional movement. Raspberry Pi 5 as the main control, Python as programming language. 1MP USB camera and 2DOF PTZ, combining with OpenCV image processing library and MediaPipe machine learning framework to realize color recognition, target tracking, license plate recognition, visual tracking, face recognition, gesture recognition, etc.

🔗 Tutorial:

Yahboom Raspbot V2 AI Large Model Robot Car

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④DOGZILLA-Lite and Rider-Pi (Built-in Raspberry Pi CM5 Core Module)

 Overview:

DOGZILLA-Lite is a 15-DOF desktop AI bionic robot dog. It is made of aluminum alloy and comes with a robotic arm and gripper, which can flexibly perform grasping tasks. Built-in Raspberry Pi CM5 module, supports face detection, object recognition, and AI visual interaction. Combined with a large model, it can not only understand instructions, but also realize free dialogue, realizing true multimodal intelligent interaction. It has advanced sports capabilities such as omnidirectional movement, six-dimensional posture control, and dynamic balance. With more than 40+ functions, from basic remote control to embodied intelligent applications, it allows learning, entertainment, and technology research to be fully upgraded. In front of the robot dog, a 2.0-inch IPS display is integrated to display 35 different expressions.Support two control methods: BT APP and WiFi APP. Whether it is programming learning, AI experiments, or creative interaction, DOGZILLA-Lite is your best partner.

Rider-Pi is a desktop two wheel-legged robot designed for developers, educators and robot enthusiasts. Built-in inertial measurement unit (IMU) and the carbon fiber connecting rod structure, which allow the robot to adjust the joint angle in real time to adapt to different terrain obstacles. Based on the Raspberry Pi CM5 core module, adopt Python programming, support a series of AI functions such as face recognition, color tracking/following, QR code motion control, object detection, license plate recognition, gesture following, etc. A 2.0-inch IPS screen on the front, can display video images and 35 dynamic expressions in real time. In addition, Rider-Pi also supports ChatGPT (extra charge), which can realize voice Q&A, voice control, text-to-picture, and image analysis description functions.

🔗 Tutorial:

Yahboom 15DOF Robot Dog DOGZILLA-Lite

Rider-Pi Two Wheel-legged Robot

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⑤MicroROS-Pi5 ROS2 Robot Car

 Overview:

This MicroROS-Pi5 ROS2 car is developed based on Raspberry Pi 5. It consists of a Micro ROS robot expansion board with ESP32 co-processor, 4PCS 310 encoder motor, high-quality tires, 7.4V 2000mAh rechargeable battery, MS200 lidar, 2MP camera, 2DOF gimbal and an aluminum alloy frame. It adopt ROS2-HUMBLE development environment and Python3 programming, and uses OpenCV image processing and MediaPipe machine learning algorithms to achieve multiple functions such as robot motion control, AI visual interaction, SLAM mapping navigation, RViz simulation and multi-machine synchronization control. Users can control it by mobile APP, wireless controller, computer keyboard control. Yahboom will provide each customer with detailed tutorial materials, installation videos and professional technical support.

🔗 Tutorial:

http://www.yahboom.net/study/MicroROS-Pi5

🎥 Video:

⑥DOFBOT SE AI Vision Robotic Arm

 Overview:

DOFBOT-SE is a 6DOF robotic arm developed by Yahboom based on a virtual machine system. It does not require a embedded development board (Jetson NANO/Raspberry Pi), generates control decisions through a PC-side virtual machine, and is driven by an STM32 controller to implement various functions. We use the ROS robot control system to simplify the complex motion control of the 6DOF serial bus servo, enabling the forward and reverse solutions, motion planning, MoveIt simulation, collision detection and other functions to be realized. It comes with a 0.3MP camera, combined with machine vision algorithms, which not only enables color recognition tracking and grabbing, but also has many functions such as model training, garbage sorting, and gesture recognition. Whether you are a beginner or an experienced developer, DOFBOT-SE is a cost-effective robotics learning kit for you.

🔗 Tutorial:

https://www.yahboom.net/study/DOFBOT_SE

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⑦JetCobot 7-axis visual collaborative robotic arm

 Overview:

JetCobot is a 7-axis visual collaborative robotic arm. It uses the NVIDIA series development board as the main control board. It adopts a configuration similar to the UR robot, flexible movement, and maximum effective arm span of 270MM. Through the ROS robot operating system and inverse kinematics algorithm, the robotic arm coordinate control, motion planning, gripping and sorting functions are realized. It equipped it with a 0.3MP USB camera, combined with OpenCV images, machine vision, deep learning and other algorithms, can complete color interaction, face tracking, label recognition, model training, gesture interaction and other functions. In addition to supporting MoveIt simulation control, JetCobot also supports handle, PC web control.

🔗 Tutorial:

https://www.yahboom.net/study/JetCobot

🎥 Video:

⑧MUTO RS ROS2 Hexapod Robot

 Overview:

Muto RS is a desktop-level  AI Large Model bionic hexapod robot developed and designed based on the ROS2 operating system and compatible with Raspberry Pi. The overall body is made of aluminum alloy and contains 18PCS 35KG serial bus servos, depth cameras, lidar, voice modules. Through Python3 programming and built-in high-precision algorithms, Muto RS can easily implement AI visual interaction, 3D mapping navigation, voice interaction, deep learning, Rviz simulation. Based on multi-machine communication technology, we can also allow Muto to complete multi-machine synchronization control and multi-machine navigation. Users can control it by APP, wireless handle, and computer web pages. Muto is not only suitable for learning algorithms for multi-legged bionic robot movement, but is also a good platform for ROS developers. 

🔗 Tutorial:

https://www.yahboom.net/study/Muto-RS

🎥 Video:

⑨ROSMASTER A1 ROS2 Robot with Ackerman Steering Chassis

 Overview:

Yahboom newly launched ROSMASTER A1 is an embodied intelligent robot car platform designed specifically for ROS education and artificial intelligence research. Built on an Ackerman steering chassis, ROSMASTER A1 accurately replicates the steering model of smart car. It integrates peripherals such as a 3D depth camera/2MP HD camera PZT (optional), T-miniPlus LiDAR, AI intelligent voice module, and multi-function ROS robot expansion board, providing the robot with stereoscopic vision comparable to the human eye and precise environmental perception. Users can choose Raspberry Pi 5, Jetson NANO 4GB, or Jetson ORIN NANO 8G as the main control board. Developed in the ROS2 HUMBLE environment, it innovatively employs a dual-model inference architecture, ensuring a clear division of labor between decision-making and execution. It seamlessly integrates visual, voice, and text information, enabling not only precise SLAM mapping and navigation, AI visual recognition, but also human-like interaction capabilities with free dialogue interruption and dynamic feedback reasoning, meeting the needs of diverse scenarios.

🔗 Tutorial:

https://www.yahboom.net/study/ROSMASTER-A1

🎥 Video: