RAI-M4

Physical AI Learning Platform

From Language to Action
Perceive. Decide. Act.

RAI-M4 combines planning, perception, and execution to deliver intelligent interaction and adaptive real-world performance.

Transforms natural language into action with multimodal perception for flexible real-world adaptation.

Equipped with omnidirectional mecanum-wheel mobility and a 4-DOF robotic arm, RAI-M4 delivers both agile movement and precise manipulation. A dual-controller architecture ensures smooth AI computing and responsive real-time control.

A practical robotics and AI platform built for hands-on learning across diverse educational scenarios.

■ Applications & Use Cases

Mobile Robot Control

AI Model Applications

Robotics

Machine Vision

ROS

Navigation Control

Localization

Key Features

AI-Powered Intelligence

Combines natural language understanding and multimodal perception for end-to-end task

planning and execution.

All-in-One Mobile Manipulation Platform

Integrates omnidirectional mobility with a robotic arm to enable flexible operation in
confined spaces.

Structured Hands-on Training Design

A modular, step-by-step learning structure designed for diverse educational

applications.

Specifications

■ Basic Specifications

OS Environment

Ubuntu 22.04 / ROS 2

Algorithm Framework

OpenCV / YOLOv8

Supported Language Models

Qwen / DeepSeek / ChatGPT / Gemini / Claude

※API Integration Support

Dimensions

≥ 240mm × 240mm

Chassis Configuration

4-Wheel Mecanum Omnidirectional Chassis

Rated Load

10kg

Maximum Speed

0.5m/s

Sensor

Built-in Gyro Sensor

Robot Arm

4-DOF Serial Arm with 1-DOF Gripper

Arm Length

240mm

Payload Capacity

300g

Control System Architecture

Host System: Task Planning, Vision Perception, Navigation Control

Low-level System: PID Control, Servo Control, Interaction Control

■ Hardware Specifications

Edge Controller

Model Number

RDK X5

Computational Performance

10 TOPS

CPU

8-core Arm Cortex-A55 processor (1.5 GHz)

BPU

10 TOPS

GPU

32 FLOPS

Memory

8GB LPDDR4

Storage

microSD Card Support

Notes

YOLOv8 face detection runs at ~100 FPS.

LiDAR Sensor

Measurement Distance

0.12～8 m

Sampling Rate

4,000 Hz

Scan Rate

5～10 Hz

Angular Resolution

0.6°～1.2°

Weight

135 g

Vision Camera

Type

High-Resolution RGB Camera

Resolution

2MP

Interface

USB 3.0

SNR

27 dB

Operating Current

80～280 mA

■ Optional Accessories

Depth Camera

Depth Module Measuring Range

0.6m～8m

Depth Module Resolution

Max 1280×720 @ 90fps, 2MP

Depth Module Field of View (FOV)

Horizontal 54.8° / Vertical 45.5°

RGB Camera Module Resolution

Max 1920×1080 @ 30fps

RGB Camera Module Field of View (FOV)

Horizontal 66.1° / Vertical 40.2°

Experiment Items

Robotic Vision

An integrated practical learning workflow covering everything from basic image processing to deep learning and multimodal perception.

■ OpenCV Vision

・Color Recognition / Shape Recognition / QR Code Recognition / Barcode Recognition

・Color Marker Detection (Integrated Processing + Filtering)

■ AI Vision – YOLO

・YOLO Model Integration and Deployment

・Dataset Annotation, Model Training, and Deployment

・Object Detection

・Face Detection

■ AI Vision – Qwen Multimodal Large Language Model

・Qwen Multimodal API Integration & Deployment

・Object Detection and Labeling

Large-Scale Model Integration and Application

Focused on end-to-end AI model practice integrating voice interaction, multimodal perception, and robotic execution.

■ Voice Interaction

　・Qwen ASR Integration and Implementation

　・DeepSeek LLM-Based Semantic Understanding

　・Volcano Engine TTS Integration and Implementation

　・LLM-Based Voice Interaction Implementation

　・Implementation of Voice-Controlled Calculator Functionality

　・Implementation of Voice-Controlled Music Playback

■ Multimodal Visual Perception

　・Qwen Multimodal API Integration and Deployment

　・Object Detection and Labeling

■ Integration with Robotic Applications

　・MCP-Based Perception and Grasping Task Planning

　・MCP-Based Navigation Task Planning

Robot Body

Robot Chassis and Arm Kinematics & Control Strategy Practice

■ Mobile Base Control

・Encoder-Based Motor PID Control

・Omnidirectional Base Kinematics Control

・Gyro-Assisted Odometry Control for Omnidirectional Mobile Base

■ Robot Arm Control

・Servo Motor Position Control

・Robot Arm Kinematics Control

・Robot Arm Trajectory Interpolation Control

ROS

Master core ROS skills including topic, service, and parameter management, as well as motion planning with MoveIt!.

■ Basic ROS Operations

・Turtlesim Control Using Topics, Services, and Parameters

・Package Porting and Execution – Keyboard Control of Turtlesim

■ Robot Arm Motion Planning with MoveIt!

　・Robot Arm URDF Configuration

　・Robot Arm Kinematic Model Configuration with MoveIt!

　・Robot Arm Motion Planning with RViz

Mobile Robot Navigation and Localization

Comprehensively covers system interfaces, mapping, and navigation workflows, enabling practical multi-point navigation implementation.

■ Mapping

・Map Project Settings

・New Map Construction

■ Navigation

・Point Navigation

・Autonomous Obstacle Avoidance Navigation

・Multi-Point Navigation