In recent years, the rapid development of Large Language Model (LLM), Computer Vision, and Embodied AI has propelled the robotics industry into a new era of intelligence.

According to data from the International Federation of Robotics (IFR), the global robotics market continues to grow, and the demand for AI robot development platforms from universities, research institutions, and enterprises is constantly increasing.

Today, whether in autonomous driving, warehousing and logistics, intelligent manufacturing, or education and research, AI robots have become an important vehicle for the practical application of artificial intelligence.

If you wish to learn about artificial intelligence, robot development, ROS2, computer vision, or robot control, understanding AI robots will be the first step.

For example, the AI robots you encounter daily include: Tesla Optimus, Figure AI, Unitree robot, Unitree robot dog, delivery robot, robotic vacuum cleaner, and AI robotic arm...

What are the components of an AI robot?

Core Components of an AI Robot

A complete AI robot system typically consists of the following layers:

Robot Body and Actuators: Robot body, motors, servo motors, robotic arms, wheeled chassis, Mecanum wheels, tracked chassis, six-legged chassis, two-wheeled chassis

Common Hardware/Technology Selections: Aluminum alloy structural components, servo motors, DC geared motors

Low-level controller: Real-time motion control and sensor data acquisition

Common Hardware/Technology Selections: STM32, ESP32, Arduino...

Upper-layer AI computing platform: Runs AI models, operating systems, and complex algorithms.

Common Hardware/Technology Selections: NVIDIA Jetson, Raspberry Pi, D-robotics RDK X5...

Sensor System: Environmental Perception and Status Feedback

Common Hardware/Technology Selections: Camera (RGB/Depth), LiDAR, IMU, Encoder

Software and Algorithm Stack: Robot Operating System, Perception, Planning and Control

Common Hardware/Technology Selections: ROS2, MoveIt, OpenCV, YOLO, MediaPipe, LLM

What technologies are needed to develop AI robots?

Python --> Linux --> ROS2 --> OpenCV --> YOLO --> MoveIt --> SLAM --> LLM --> Embodied AI

Python – The universal language of the AI era, the interface foundation for all mainstream frameworks

Linux – The mainstream operating system environment for robotics development

ROS2 – The de facto standard for robotics software development

OpenCV – The cornerstone of image processing and visual perception

YOLO – The core of real-time object detection algorithms

MoveIt – A framework for robotic arm motion planning and control

SLAM – Simultaneous localization and mapping, the foundation of mobile robots

LLM – Large language models, endowing robots with natural interaction and task reasoning capabilities

Embodied AI – A cutting-edge direction combining cognitive models with physical entities

Four Future Trends in AI Robots

Embodied Intelligence as a Core Direction

The deep integration of the AI model's "brain" and the robot's physical "body" will enable robots to understand, reason, and perform complex physical tasks. Predictions show that by 2035, over 2 million humanoid robots will be deployed in workplaces worldwide.

From Lab to Large-Scale Commercialization

AI robots are moving from research demonstrations into real-world scenarios such as industry, logistics, retail, and healthcare, with the industry entering the commercialization phase.

AI-Enabled "Autonomous Programming"

Future robots may be able to autonomously generate control code based on natural language task descriptions, significantly lowering the development threshold.

Dedicated AI Acceleration Hardware Becomes Standard

To efficiently run AI models under power constraints, dedicated accelerators (such as the NPU/GPU in Jetson) will become standard hardware for robots.

Recommended AI Robotics Learning Platforms

① ROSMASTER M3 PRO

Recommended learning direction: ROS2, SLAM, Navigation2, AI Vision, MoveIt

Target audience: Composite secondary development, warehouse logistics company research, robotics competitions...

② DOFBOT-Ultra robotic arm

Recommended learning direction: Visual grasping, MoveIt, inverse kinematics, embodied intelligence applications

Target audience: Robotic arm developers, AI algorithm researchers

③ Raspberry Pi AI Vision Robot 

Recommended learning direction: Python, OpenCV, YOLO, basic robot control, Deep learning deployment

Target audience: AI ROS beginners, educational settings, Raspberry Pi fans

④ 12DOF Robot Dog DOGZILLA

Recommended learning direction: Complex semantic speech recognition, natural language dialogue, Embodied intelligence, Multimodal large language model

Target audience: Edge AI developers, advanced researchers, Bionic robot development...

FAQ

Q1: What are the differences between AI robots and ordinary robots?

The core difference lies in whether or not a robot possesses a "brain" and the ability to learn autonomously.

Traditional robots (or ordinary robots): are more like "tools." They are typically programmed to repeatedly perform a specific task, lacking the ability to learn independently or adapt to their environment. Examples include robotic arms in factories that perform only single actions, and AGVs that follow fixed routes.

AI robots (intelligent robots): are more like "partners." Through an AI model—their "brain"—they can perceive their environment, understand instructions, and make autonomous decisions. They possess learning capabilities and can cope with changing environments. Examples include humanoid robots that can engage in natural conversation, maintain balance on different terrains, and autonomously plan routes.

In short, traditional robots are "highly specialized," while AI robots are "more flexible."

Q2: Does learning Python require learning AI robots?

Yes, Python is necessary, and it's the preferred language. It's almost indispensable, especially during the learning and prototyping phases.

The core of the ecosystem: Mainstream AI frameworks, such as PyTorch, TensorFlow, and LangChain, all use Python as their development interface. If you want to call AI models, you essentially can't do without it.

High development efficiency: Python's concise syntax allows you to quickly turn your ideas into code. Whether processing sensor data or calling large model APIs, Python is very convenient.

More than just "writing code": Even if you use AI tools to assist programming in the future, you'll still need to know Python to understand, debug, and optimize AI-generated code; this is the foundation for collaborating with AI.

Q3: What hardware is needed to learn ROS2?

This depends on your budget and goals. ROS2 itself doesn't have high hardware requirements, but combining it with AI features is another story.

Introductory Learning Plan: A decent computer is sufficient. Install Ubuntu and ROS2 using a virtual machine or Docker. With some simulation software, you can learn most of the theoretical knowledge.

Real Robot Development: Robots typically employ a layered architecture: "Upper-level AI decision-making (host computer) + Lower-level motion control (lower-level computer)."

Host Computer: Needs to run Linux and ROS2 and have sufficient AI computing power. A high-performance ARM development board is a common choice.

Lower-level Computer: Responsible for real-time motor control and sensor data acquisition. Commonly used are microcontrollers (MCUs) like STM32 and GD32, running a real-time operating system (RTOS).

Q4: Can a Raspberry Pi be used to develop AI robots?

Yes, but it's more suitable for lightweight projects, learning, and prototyping.

What it can do: The Raspberry Pi can run a full Linux system and ROS2, which can be used for some basic robotics projects. Some research has implemented neuromorphic robot systems based on the Raspberry Pi, and some projects use it as a control server.

Limitations: Its main weakness is AI computing power. It struggles with complex deep learning models (such as object detection and large language models). If you want to build robots that require powerful visual recognition or autonomous decision-making, it may not be sufficient.

Q5: Is Jetson suitable for robot development?

Ideally suited for high-end AI robot development, Jetson is currently the mainstream choice, practically born for robotics.

Top-tier AI computing power: The Jetson series, especially the Orin and Thor models, offers powerful GPU computing power (up to tens to hundreds of TOPS), enabling smooth operation of complex deep learning models.

Dedicated accelerators: In addition to GPUs, Jetson also has dedicated hardware accelerators for processing visual tasks such as image processing and optical flow, offering high energy efficiency.

Comprehensive ecosystem: NVIDIA provides the JetPack SDK and Isaac robotics development kit, with excellent compatibility with ROS2, greatly accelerating development. Leading robotics companies like Boston Dynamics also use the Jetson platform.

Q6: What are the future development directions of AI robots?

In summary, AI robots are evolving from "active machines" to "intelligent agents capable of thinking and acting in the physical world."

"Embodied intelligence" is a core trend: combining the large-scale model "brain" of AI with the physical "body" of the robot, allowing it to understand and interact with the world like a human. It is projected that by 2035, the number of humanoid robots in the workplace will exceed 2 million.

From the laboratory to industrial and service scenarios: AI robots are rapidly moving from the research and demonstration stage to real factories, warehouses, hotels, and even homes, solving practical business problems. After a round of industry reshuffling, it will enter a more pragmatic commercialization phase.

AI enables robots to "program autonomously": In the future, generative AI may allow robots to write their own control code based on task descriptions, greatly reducing the development threshold.

Dedicated hardware accelerators become standard: To efficiently run complex AI models on power-constrained robots, dedicated hardware accelerators like Jetson will become an industry necessity.

Start Your AI Robot Development Journey

AI robotics is a multidisciplinary field integrating algorithms, software, hardware, and control. It's recommended to choose a suitable starting point based on your background, beginning with Python and Linux, and gradually progressing to ROS2, perception algorithms, and AI deployment.

Yahboom offers a full range of AI educational robot platforms covering beginner to advanced levels, including ROSMASTER M3, DOFBOT Ultra, Jetson AI Robot, and Raspberry Pi AI Robot, fully supporting mainstream technology stacks such as ROS2, OpenCV, YOLO, and MoveIt.

Whether you are a student, teacher, researcher, or industry developer, you can find a suitable learning platform here to take your first step in AI robot development.