Yahboom DOFBOT SE 6DOF Robotic Arm Virtual Machine Version, ROS2 Humble Python AI Vision Large Model Kit
Yahboom DOFBOT SE 6DOF Robotic Arm Virtual Machine Version, ROS2 Humble Python AI Vision Large Model Kit
Yahboom
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Overview
DOFBOT SE is an AI large model vision robotic arm (virtual machine version) from Yahboom. It is a 6 DOF desktop robotic arm that generates control decisions on a PC-side virtual machine and drives the joints through an STM32 controller. The system is designed around ROS2 to support kinematics (forward/inverse), motion planning, MoveIt simulation, and collision detection, while also enabling AI vision interaction for tasks such as color recognition tracking and object grasping.
Key Features
- PC virtual machine main control: uses a PC virtual machine to replace ARM development boards; Mac not supported.
- ROS2 development: ROS2 robot control system (image specifies ROS2 Humble), supporting motion planning, simulation, and related workflows.
- 6 DOF serial bus servo control: integrated control for multi-joint motion and grasping.
- AI vision interaction: plane 2D vision with functions such as color recognition/tracking/grabbing and gesture recognition.
- AI large model interaction (feature availability depends on version): images describe multimodal capabilities (text/voice/vision) including scalable RAG knowledge base, natural voice dialogue, and visual scene understanding.
- Multiple control methods: text states mobile app, wireless handle, and PC software control.
Specifications
| Model | DOFBOT SE (Virtual machine version) |
| Master control | PC virtual machine |
| Degree of freedom | 6 |
| Arm span | 350mm |
| Gripper open-close angle | 6cm |
| Repeatable positioning accuracy | ±0.5mm |
| Structure type | Traditional robotic arm structure |
| Camera | USB HD camera |
| Visual dimension | Plane 2D image |
| Voice | AI large model voice module + speaker |
| Display | / |
| Functions (image text) | Interconnection control; MoveIt motion planning; Rviz robot simulation; 2D visual interaction; voice interaction; AI large model |
| Positioning (image text) | Virtual machine version entry-level AI large model robotic arm |
| ROS system (image text) | ROS2 Humble |
Camera resolution note: the provided text description mentions a 0.3MP camera, and also states a 30MP camera module under “Professional hardware configuration”. Images specify a USB HD camera but do not show a megapixel value. For confirmation of the exact camera module shipped, contact support@rcdrone.top or visit https://rcdrone.top/.
Software & Control
- ROS2 control: simplifies 6 DOF serial bus servo motion control; supports forward solution, inverse solution, and motion planning (text).
- MoveIt + RViz: images list MoveIt motion planning and RViz robotic arm simulation.
- Virtual machine serial communication: images describe sending instructions from the virtual machine to an STM32 co-processor through a serial port to drive each joint.
- Remote control methods (text): mobile app, wireless handle, and PC software.
AI Vision, Gesture, and Model Training (as described)
- AI visual recognition/target tracking: color recognition & tracking; color grabbing; color interaction; waste sorting; color block stacking; “grab game”.
- Gesture interaction (MediaPipe): gesture control robotic arm action group; gesture recognition control stack; attitude control robotic arm; robotic arm recognition and tracking of palm.
- Algorithm frameworks (image text): inverse kinematics algorithm; YOLOv11; OpenCV; MediaPipe.
- Deep learning & model training (image text): supports custom training and model quantification deployment; examples shown include garbage classification and model training (YOLOv11 case presentation).
Notes shown in images: “The trash can is not included in the shipping list.” “Model training requires users to train themselves.”
Version Differences (image text)
| Standard Version | Superior Version | |
| Support main control | PC Virtual Machine | |
| AI large model voice module | No | Yes |
| AI large model function | No | Yes |
| AI visual interaction | Yes | Yes |
| ROS system | ROS2 Humble | |
| Recommended users | Suitable for learning AI visual functions | Suitable for learning AI large model, AI voice interaction, and AI visual function applications |
Applications
- ROS2 learning and research: kinematics, motion planning, MoveIt simulation, and collision detection.
- AI vision demos: object tracking, color recognition, grasping and placement, and sorting workflows.
- Multimodal interaction demos (as described): video parsing, long command action control, intelligent handling, and 3D space sorting.
Tutorials & Videos
Tutorial link: Yahboom DOFBOT SE Robotic Arm
Details
DOFBOT SE pairs a 6‑DOF desktop arm with PC-side virtual machine control and ROS2 Humble workflows for learning and development.
Virtual machine main control reduces hardware dependency while keeping core functions like motion planning, RViz simulation, and 2D visual interaction.
A higher configuration adds depth-based 3D visual interaction for spatial perception and grasping applications.
Other arm options in the series highlight different DOF and structure choices for specific research and teaching needs.
Compared with ARM development boards, the PC virtual machine approach emphasizes easier expansion, backup/restore, and cost-effective development.
Built for ROS2 development, DOFBOT SE supports AI vision interaction and multi-joint servo control in a compact desktop form.
Multimodal interaction, visual applications, and step-by-step tutorials help move from first motion to practical AI vision tasks.
Choose the configuration that matches your needs, from ROS2 Humble basics to expanded AI large model capabilities.
Large language, voice, and vision models enable more natural command and feedback during robotic arm operation.
Application demos focus on turning natural commands into repeatable actions like handling and sorting sequences.
Multimodal understanding supports varied classroom-style scenarios such as Q&A, analysis, and guided task execution.
2D vision functions include color recognition, target tracking, and guided grasping for interactive tabletop projects.
Gesture recognition and model training content extends interaction beyond the gripper for richer AI control experiments.
ROS2 kinematics plus MoveIt simulation helps validate trajectories before running motions on the real arm.
ROS2 Humble support and multiple control methods—mobile app, PC control, and wireless handle—fit different learning setups.
The DOFBOT SE app includes gesture grabbing along with remote control, tracking, servo calibration, and sorting modes for flexible operation.
The DOFBOT SE robotic arm supports face recognition tracking and custom action group learning for repeatable motion sequences.
The DOFBOT SE 6-DOF robotic arm kit features a USB camera, STM32 core board, and clearly labeled J1–J6 joints, with optional speaker and AI voice module support.
The DOFBOT SE kit combines a 6DOF servo-driven arm with an expansion board that provides clearly labeled ports and headers for wiring peripherals and power.
The DOFBOT SE kit includes a USB camera module, a 2.4G wireless handle with receiver board, and an AI voice module with speaker and wiring for easy integration.
The DOFBOT SE course outline covers setup, calibration, remote control, and programming lessons, including AI vision modules.
The kit’s learning outline covers large model basics, environment setup, and practical projects like configuring an API key and integrating AI features.
The kit includes structured open-source code resources and step-by-step video tutorials covering AI large-model features and ROS2 basics.
The DOFBOT SE kit includes access to a simple 3D model file and provides technical support with after-sales service.
DOFBOT SE includes a millimeter dimension outline and a quick spec list covering the onboard platform and software environment such as Ubuntu 22.04 with ROS2 Humble and Python.
The DOFBOT SE kit includes the robotic arm body and chassis, STM32 core board, camera with bracket, 2.4G wireless handle, cables, tools, and an instruction manual.
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