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Jetson Orin NX SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notes
  • 04.Disabling automatic updates and troubleshooting issues
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Write Jetson original system
  • 03.Installing Jetson environment
  • 04.Backup SSD system
  • 05.Restore initial image system
  • 06.Upgrade to SUPER version
  • 07.Write SUPER boot
  • 08.SSD expansion
  • 09.Jetpack old system upgraded to Yahboom SUPER factory system
  • 10.Write the system onto your own solid-state drive
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.Vision basic course
  • 01.CSI camera preview
  • 02.USB camera preview
  • 03.Use Jupyter Lab
  • 04.Use JetCam
06.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
  • 17.Preview USB camera image
  • 18.Preview CSI camera image
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.QR code recognition
06.YOLO26 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detectionnew
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS1-Melodic
01.ROS1 basic course
  • 01. Introduction to ROS
  • 02. Project file structure
  • 03. Common commands and tools
  • 04. Publisher
  • 05. Subscribers
  • 06. Customize topic messages and use
  • 07. Client
  • 08. Server
  • 09. Customize service messages and usage
  • 10. TF release and monitoring
02.ROS1 vision course
  • 00.Must read before running
  • 01.Camera preview
  • 02.QR code
  • 03.Human posture estimation
  • 04.Target detection
  • 05.AR vision
  • 06.Opencv application
  • 07.ROS+Opencv basics
  • 08.Mediapipe development
10.ROS2-Humble
01.ROS2 basic course
  • 01.Introduction to ROS2
  • 02.ROS2 install Humble
  • 03.ROS2 development environment
  • 04.ROS2 workspace
  • 05.ROS2 function package
  • 06.ROS2 node
  • 07.ROS2 topic communication
  • 08.ROS2 service communication
  • 09.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera preview (CSI)
  • 03.Camera calibration (USB)
  • 04.Camera calibration (CSI)
  • 05.AR vision (USB)
  • 06.AR vision (CSI)
11.Offline AI model coursenew
Offline large model (Text Interaction)
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
12.Online AI model coursenew
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
13.NVIDIA lsaac ROS coursenew
  • 1.Build lsaac ROS environment
  • 2.Deep segmentation
  • 3.DNN stereoscopic depth
  • 4.Free space segmentation
  • 5.Image distortion processing
  • 6.Image segmentation
  • 7.3D scene reconstruction and mapping
  • 8.Object detection
  • 9.3D attitude estimation
  • 10.Visual SLAM
14.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start on Jetson Platform
  • 02.OpenClaw Deployment
  • 03.OpenClaw WhatsApp Plugin Application
  • 04.OpenClaw WebChat Interaction
  • 05.OpenClaw TUI Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawHub Introduction (Skill Installation)
  • 10.OpenClaw Application - File Management
  • 11.OpenClaw Application - Camera
  • 12.OpenClaw Application - Browser Control
  • 13.OpenClaw Application - Script Execution
  • 14.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 15.OpenClaw Application - Dedicated AI Assistant
  • 16.OpenClaw Web Search Capability Configuration
15.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 1. Peripheral Hardware Configuration
  • 2. OpenClaw API Key Configuration
  • 3. OpenClaw Model Switching
  • 4. OpenClaw Prompts
  • 5. AI Voice Interaction Configuration
  • 6. Three Dialogue Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
Download
  • System_File
  • VM_File
  • 3D_Model_File
  • Data_Sheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
Download ZIP

Welcome to Jetson Orin NX SUPER repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

2. RGB Strip

OpenClaw RGB LED Control Tutorial

Before starting, please complete the following in the Pre-use Preparation directory: [01-Peripheral Hardware Configuration], [02-OpenClaw API-KEY Configuration], [03-OpenClaw Switching Models], [04-AI Voice Interaction Configuration], [05-Three Chat Modes Configuration Testing]. If you are currently only using TUI / WhatsApp, you can skip 04 for now. This document assumes the basic environment is already prepared and only covers RGB-related content.

1. Tutorial Objectives

This tutorial demonstrates OpenClaw's natural language control capability through RGB LED peripherals. After completing this tutorial, readers will be able to:

  • Understand the basic approach of OpenClaw controlling RGB LEDs
  • Chat with OpenClaw through three methods: WhatsApp, TUI, and voice interaction
  • Complete four RGB examples: single color lighting, breathing light, warning flash, rainbow marquee
  • Understand the complete pipeline from "user input" to "light execution"

2. Hardware Preparation

  • OpenClaw main control board
  • RGB LED strip / RGB LED module [default 14 WS2812 LEDs]
  • [Optional] Microphone module
  • Wiring diagram

You can add your own RGB wiring diagram and hardware photo here.

3. Software Preparation

Code entry points used in this tutorial:

  • Low-level driver code: /home/jetson/hardware_hal_demo/
  • RGB control command: /home/jetson/hardware_hal_demo/rgb
  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py

The rgb tool already supports the following capabilities:

  • Set fixed color color
  • Set light effect effect
  • Turn off lights off

Commonly used light effects include:

  • breathe breathing light
  • blink flash
  • flow flowing light
  • marquee marquee light
  • rainbow rainbow light

If you want to individually verify the low-level RGB commands, you can also enter the following in the terminal:

rgb color --color red  # All lights turn red
rgb effect --effect breathe --color blue --speed 5   # Breathing light
rgb off   # Turn off lights

For more commands, see the markdown documentation in this folder:

xxxxxxxxxx
/home/jetson/hardware_hal_demo/README.md

If the low-level commands can execute normally, it means the hardware connection to the Jetson Orin NX is correct and communication is established.

4. Testing RGB LEDs

Before starting this tutorial, please complete a basic openclaw tui chat self-check; if you haven't done so, see [05-Three Chat Modes Configuration Testing] first. After confirming OpenClaw can reply normally, proceed to the RGB-specific test.

Enter the following command in the terminal to enter TUI:

xxxxxxxxxx
openclaw tui

image-20260529190932175

You can try entering the following in the terminal:

  • Set all RGB LEDs to red
  • Change the RGB LEDs to blue
  • Lower the RGB LED brightness a bit
  • Turn off the RGB LEDs

image-20260427171923502

If you want to test individual LEDs, you can also try entering content like:

  • Set LED 1 to green
  • Set LED 5 to purple

5. Three Methods to Chat with OpenClaw

MethodSuitable ScenariosAdvantagesRecommended Positioning
WhatsAppRemote control, classroom demoEasy to connect, suitable for multi-person experienceDemonstrate "remote natural language control"
TUILocal debugging, command verificationMost direct, easiest to troubleshootFirst get the control pipeline working
Voice CodeVoice interaction, complete AI experienceClosest to real conversation experienceAs the final demonstration method

If the AI voice module + speaker is an optional add-on, Feishu chat, TUI, and WhatsApp chat need to play back OpenClaw's replies. You can run the following program in the terminal. AI voice module chat does not need to run it again.

  • Modify the broadcast parameter
xxxxxxxxxx
/home/jetson/voice_to_openclaw/.env
xxxxxxxxxx
ENABLE_TTS=true  # This parameter controls whether to broadcast. After modification, save and run the following program in the terminal
  • Enter the following in the terminal
xxxxxxxxxx
cd ~/voice_to_openclaw
source .venv/bin/activate
python -m src.openclaw_session_tts

image-20260529190948629

5.1 whatsapp Method

For WhatsApp connection steps, please refer directly to [05-Three Chat Modes Configuration Testing]. This section only retains chat examples suitable for the RGB tutorial.

After creation, you can chat directly, for example:

  • Change the LED strip to blue
  • Turn on the rainbow light effect
  • Make the lights flash faster

image-20260427172818119

5.2 TUI Method

If you have completed the TUI self-check in Part 0, simply enter the following command in the terminal to continue chatting:

xxxxxxxxxx
openclaw tui

image-20260529190959032

5.3 Voice Code Method

xxxxxxxxxx
Requires optional AI voice interaction module + speaker

For voice module installation, environment configuration, and basic wake process, please refer directly to [04-AI Voice Interaction Configuration]. After waking up, you can speak your control commands directly, for example:

  • Change the lights to red
  • Turn on the blue breathing light
  • Make the lights flow like a rainbow

image-20260529191037239

6. Comprehensive Cases

Note: The following are all demonstrated using openclaw tui for chat. You can also choose WhatsApp or voice methods to complete them.

6.1 Case 1: Single Color Lighting

Experimental Objective

Make the RGB LEDs consistently display a single fixed color, completing the most basic light control.

Effect Description

After receiving the command, all RGB LEDs uniformly display the specified color, such as red, green, blue, white, or warm white. This case is suitable for verifying that color mapping is working correctly.

Example Commands

  • Set all RGB LEDs to red
  • Change the LED strip to blue
  • Change the lights to warm white

6.2 Case 2: Breathing Light

Experimental Objective

Make the RGB LEDs gradually vary in brightness on a given color, creating a breathing light effect.

Effect Description

The RGB LEDs will cycle from dim to bright, then from bright to dim. The overall effect is relatively soft, suitable for standby indicators, ambient lights, or status indicator lights.

Example Commands

  • Turn on the blue breathing light
  • Change the lights to a green breathing effect
  • Slow down the breathing light speed a bit

6.3 Case 3: Warning Flash

Experimental Objective

Make the RGB LEDs rapidly flash to simulate a warning light or alarm effect.

Effect Description

The lights can flash rapidly using red, yellow, or alternating red and blue, suitable for expressing information like "attention", "warning", or "abnormal state", with more obvious visual feedback.

Example Commands

  • Make the lights flash red
  • Turn on the yellow warning light
  • Make the RGB LEDs flash rapidly three times

6.4 Case 4: Rainbow Marquee

Experimental Objective

Make the RGB LEDs form a continuously flowing dynamic effect, demonstrating richer light expression.

Effect Description

The LED colors will change sequentially and flow in one direction, looking like a rainbow flowing light or marquee light. This effect is very suitable for boot animations, demonstration animations, or interactive light effects.

Example Commands

  • Turn on the rainbow light effect
  • Make the lights flow like a marquee
  • Turn on the blue flowing light, with a faster speed
Servo Control
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