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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。

6. AI Script Peripheral Control

OpenClaw AI Script Writing for Peripheral Control Tutorial

Before starting, please complete the following in the Pre-use Preparation directory: [01-Peripheral Hardware Configuration], [02-OpenClaw Ajetson-KEY Configuration], [03-OpenClaw Model Switching], [04-AI Voice Interaction Configuration], [05-Three Dialogue Scheme Configuration Test]. If you are currently only using TUI / WhatsApp, you can skip 04 for now. This article assumes the basic environment is already prepared and only covers the AI script generation feature.

1. Tutorial Objective

This article demonstrates through "natural language hardware script generation" that OpenClaw can not only directly control peripherals but also generate reusable script files from your control logic. After completing this tutorial, readers will be able to:

  • Understand the basic approach of OpenClaw generating hardware control scripts
  • Interact with OpenClaw through three methods: WhatsApp, TUI, and voice interaction
  • Generate four types of common scripts: RGB, servo, temperature and humidity, OLED

2. Hardware Preparation

  • OpenClaw main control board
  • Servo / RGB light strip / OLED / DHT temperature and humidity sensor
  • [Optional] AI voice module + speaker

Description:

  • This tutorial does not require connecting all peripherals at once
  • Connect the corresponding hardware first depending on which type of script you plan to demonstrate

3. Software Preparation

Code entry points used in this tutorial

  • Script generation skill: /home/jetson/.openclaw/workspace/skills/script-runner/SKILL.md
  • Default script save directory: /home/jetson/Desktop/
  • Hardware command examples directory: /home/jetson/hardware_hal_demo/
  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py

The core of this capability is not "executing an instant command" but having OpenClaw generate a standalone Python script for you. According to the existing skill conventions:

  • When the user says "generate script", "write a script", or "save to desktop", it will prioritize using script-runner
  • Hardware scripts use openclaw_lib by default
  • The default serial port is /dev/ttyTHS1
  • The default baud rate is 115200
  • Scripts are typically saved in /home/jetson/Desktop/

You can understand this as two usage modes:

  • Mode 1: Generate script and run immediately
  • Mode 2: Only generate the script and save it to the desktop for later modification or execution

4. Testing Script Generation Capability

Before starting this tutorial, please complete a basic openclaw tui dialogue self-check first; if you haven't done so, see [05-Three Dialogue Scheme Configuration Test]. After confirming that OpenClaw can reply normally, proceed to the script generation-specific test.

Enter the following command in the terminal to enter TUI:

openclaw tui

image-20260529193959497

It is recommended to start with the simplest script requests, for example:

  • Help me write a script to make the RGB red light blink 5 times, save to desktop and run
  • Generate a script to read temperature and humidity, don't run it yet
  • Write a script to make servo 1 swing back and forth between 0, 90, and 180 degrees
  • Generate a script to display "Hello OpenClaw" on the OLED

5. Three Schemes for Communicating with OpenClaw

SchemeSuitable ScenariosAdvantagesRecommended Use
WhatsAppRemote requests, classroom demonstrationCan describe the desired script in natural languageUsed to demonstrate natural language generation capability
TUILocal debugging, verify generation resultsEasiest to observe script generation and execution outputPreferred debugging scheme
Voice codeVoice interaction, complete AI experienceCan verbally describe script needsAs a complete demonstration scheme

If you have the optional AI voice module + speaker, Feishu dialogue, TUI, and WhatsApp dialogue need to play back OpenClaw's replies via TTS. You can run the following program in the terminal. The AI voice module dialogue does not need to run this repeatedly.

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

image-20260529194025490

5.1 whatsapp Scheme

For WhatsApp connection steps, please directly refer to [05-Three Dialogue Scheme Configuration Test]. This section only includes dialogue examples suitable for the script generation tutorial.

You can directly converse, for example:

  • Generate a script for the servo to swing left and right for 10 seconds and run it

image-20260428185931543

5.2 TUI Scheme

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

xxxxxxxxxx
openclaw tui

Then you can directly enter script requests, for example:

  • Help me write a script to control RGB to become a blue breathing light and run it

    image-20260428190049521)

5.3 Voice Code Scheme

xxxxxxxxxx
Requires optional AI voice interaction module + speaker

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

  • Write a script to display the current temperature and humidity on the screen and run it

image-20260529194118400

6. Comprehensive Cases

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

6.1 Case 1: RGB Blink Script

Experimental Objective

Have OpenClaw generate a basic light script to verify the "script creation + peripheral execution" chain.

Effect Description

OpenClaw will organize your natural language requirements into a Python script. After the script is saved to the desktop, you can run it directly or keep it for later modification.

Example Instructions

  • Help me write a script to make the RGB red light blink 5 times
  • Generate a blue breathing light script, save to desktop and run
  • Write a script to make RGB do a rainbow light effect

6.2 Case 2: Servo Action Script

Experimental Objective

Generate a servo action script to turn a one-off conversational action into a reusable script.

Effect Description

This type of script is suitable for actions like "nod", "shake head", "swing back and forth", and "return to center", and is also suitable for further expansion into classroom demonstrations or combined projects.

Example Instructions

  • Write a script to make servo 1 rotate back and forth among 0, 90, and 180 degrees
  • Generate a servo nodding script
  • Help me write a script to make four servos return to 90 degrees together

6.3 Case 3: Temperature and Humidity Reading Script

Experimental Objective

Generate a script that reads the current temperature and humidity and outputs the results.

Effect Description

This type of script is suitable for sensor self-checks, data sampling, or subsequent log recording, and is also the most commonly used basic script type for scheduled tasks later.

Example Instructions

  • Generate a script to read temperature and humidity, save to desktop
  • Help me write a script to print the current temperature and humidity
  • Write a script to read DHT data once and run it

6.4 Case 4: OLED Display Script

Experimental Objective

Generate a script that displays text or metric content on the OLED.

Effect Description

This case is suitable for welcome pages, status pages, and simple dashboards, and is also the foundation for integrating with temperature/humidity sensors, cameras, and other capabilities later.

Example Instructions

  • Help me write a script to display "Hello OpenClaw" on the OLED
  • Generate a script to display temperature and humidity on the OLED
  • Write a script to display a page of simple status information

6.5 Case 5: Combined Script

Experimental Objective

Have OpenClaw generate a comprehensive script that includes multiple peripheral actions at once.

Effect Description

This type of script can chain together "read sensor", "display on OLED", "control RGB", and "drive servo", making it suitable for transitioning from single peripheral control to small project scripts.

Example Instructions

  • Write a comprehensive script: read temperature and humidity then display on OLED
  • Generate a script: first turn on green light, then rotate servo to 90 degrees
  • Help me write a script: if temperature is too high, turn on red light and show alert on OLED
Camera Application
Plant Care Assistant

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