Physical AI Education Kit

RAI-P4

Embodied AI Task Planning Training Platform
From learning to practice — putting intelligence in motion.

RAI-P4

Overview

From Learning to Practice, Bringing Intelligence into Action

RAI-P4 is an embodied AI training platform that deeply integrates artificial intelligence and robotics technologies.
The system integrates AI voice, AI vision, a 4-DOF robotic arm, multiple sensors, and an AI embedded control board to provide a real-robot environment for implementing and debugging intelligent algorithms, seamlessly bridging theory and practice.

This platform provides diverse experimental scenarios based on a three-step process: embodied task planning, perception and decision-making, and action execution.
It covers a broad set of capabilities, including voice interaction, semantic task understanding, vision-based pan-tilt tracking, vision-guided robotic arm manipulation, kinematic trajectory control, and AI vision-based fruit and vegetable detection.

■ Applications & Use Cases

AI & Robotics EducationLarge Model ApplicationsMachine LearningMachine VisionROSEmbedded DevelopmentSensing and ControlSystem Integration

Features

Key Features

Task Planning Flowchart

System Integration of AI and Robotics

Built around an intelligent robotic arm, the system integrates an AI voice interface, AI vision perception, and an AI embedded control board.

In addition, integrating devices such as a color recognition sensor and an attitude sensor, it enables educational training that covers the full pipeline from perception and decision-making to execution.

One-Stop Deployment

Fully calibrated at shipment, enabling immediate operation without additional equipment.
Featuring a compact 60 cm × 60 cm footprint, the system is designed for flexible use in laboratories and mobile classroom settings.

Step-by-Step Robot Arm Platform

Built on a 4-DOF robotic arm platform, with support for expansion to 6 DOF.
Students progressively develop system design skills through kinematics, control, simulation, and ROS-based practice.

Visual Manipulation and Kinematics Integration Training

Specifications

Specifications

Basic Specifications

Robot Arm4-DOF Serial Arm with 1-DOF Gripper
Arm Length260mm
Payload Capacity300g
Gimbal Module2-DOF Gimbal with High-Resolution Camera Module
Monocular Camera Module720P / 2 Megapixels
Display Module13 inch
RDK X5 BoardROS & Ubuntu (Pre-installed) / 10 TOPS Performance
Microphone and Speaker ModuleFor Voice Interaction
Wearable Gesture ModuleEquipped with a gyroscope sensor, with a wearable strap for mounting.
Keyboard and MouseLogitech Wireless Bluetooth Keyboard and Mouse
Controller ModuleWireless Controller

Software Configuration

Ubuntu, ROS2, and other essential software are pre-installed, allowing immediate development and demonstration right after unboxing.
Compatible with major frameworks including OpenCV, YOLO, and LLM SDKs, making it suitable for a wide range of applications from education to research.

Sensor configuration

Integrates a comprehensive set of feedback sensors for AI voice, visual perception, and robotic motion, enabling multimodal inputs for embodied task planning.

  Microphone Array for AI Voice Interface

  Pan-Tilt Camera Module for Vision

  Color Recognition Sensor

  Attitude Sensor

  Object Recognition and Distance Sensing Expansion Interface

Controller Configuration

Built with an AI embedded control board and open I/O interfaces, enabling large-scale model inference and execution of vision algorithms.
It also enables robotic arm actuation and integration with various external devices, achieving unified hardware–software coordinated control.

Curriculum

Experiment Items

Large-Scale Model Integration and Application

■ Automatic Speech Recognition (ASR)

■ Natural Language Understanding (NLU) / LLM

■ Text-to-Speech (TTS)

■ Calculator Interaction via Function Calling

■ Music Playback Interaction via Function Calling

■ Task Planning for Cloud Vision System via Function Calling 

■ Task Planning for Robot Vision Systems via Function Calling

 

Robot Vision and Machine Learning

■ Color Recognition

■ Shape Recognition

■ YOLO Deployment

■ Face Detection

■ Face Tracking and Workspace Detection

■ Vision-Based Workpiece Picking

 

 

Fundamentals of Robotics

■ Kinematics-Based Fixed-Point Motion for Robotic Arms

■ Kinematics-Based Linear Interpolation for Robotic Arms

■ Kinematics-Based Circular Interpolation for Robotic Arms

 

 

ROS

■ ROS Fundamentals

■ Keyboard-Based Robot Arm Control

■ Robot Arm Motion Planning with MoveIt!

■ Vision Detection Workstation

 

Sensor Detection and Control

■ Pose Detection

■ Robot Arm Control via Attitude Control

■ Vision Detection

■ Vision Tracking

■ Vision-Guided Manipulation

 

Embedded Systems Development and Applications

■ Linux Basics

■ Serial Communication Experiment

■ Socket Communication Experiment

■ Multithreading Experiment

■ GUI Design Experiment

 

Contact

Consultation on Deployment, Demos and Quotes

From product selection to integrating robots into your classes or research, feel free to contact us.

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