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Jetson ORIN NANO SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notesnew
  • 04. Disable automatic updates and troubleshooting issues
  • 05. Notes on Jetson Orin Nano Official Kitnew
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.Write SUPER original system(optional)
  • 07.Write SUPER boot (optional)
  • 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
  • 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
  • 01. Peripheral Hardware Configuration
  • 02. OpenClaw API-KEY Configuration
  • 03. OpenClaw Model Switching
  • 04. OpenClaw Prompts
  • 05. AI Voice Interaction Configuration
  • 06. 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
  • DataSheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
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Welcome to Jetson ORIN NANO 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 [01-Hardware Configuration], [02-openclaw API-KEY Configuration], [03-openclaw Model Switching], [04-AI Voice Interaction Configuration], [05-Three Dialogue Solution Configuration Tests] under the Prerequisites directory. If you are currently only using TUI / whatsapp, you can skip 04 for now. This article assumes the basic environment has been prepared and only expands on "letting AI generate independently runnable scripts".

1. Tutorial Objectives

This article demonstrates through "natural language generation hardware scripts" that OpenClaw can not only directly control peripherals, but also help you save control logic as script files. After completing this tutorial, readers can:

  • Understand the basic approach to OpenClaw generating hardware control scripts

  • Communicate with OpenClaw through three methods: whatsapp, TUI, and voice code

  • Generate four common script types: RGB, servo, temperature and humidity, OLED

2. Hardware Preparation

  • OpenClaw mainboard

  • 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 for the type of script you want to demonstrate

3. Software Preparation

Code entry points that will be used in the tutorial

  • Script generation skill: /home/jetson/.openclaw/workspace/skills/script-runner/SKILL.md

  • Default script save directory: /home/jetson/Desktop/

  • Hardware command example directory: /home/jetson/hardware_hal_demo/

  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py

The core of this type of capability is not "executing an immediate command", but letting OpenClaw help you generate an independently runnable Python script. According to existing skill agreements:

  • When users say "generate script", "write a script", "save to desktop", they will prioritize using script-runner

  • Hardware scripts use openclaw_lib by default

  • Default serial port is /dev/ttyTHS1

  • Default baud rate is 115200

  • Scripts are usually saved in /home/jetson/Desktop/

You can understand it as two usage methods:

  • Method one: Generate script and run immediately

  • Method two: Only generate script, save to desktop first, modify or run again later

4. Test Script Generation Capability

Before starting this tutorial, please complete one openclaw tui basic dialogue self-check; if you haven't done it yet, see [05-Three Dialogue Solution Configuration Tests]. After confirming OpenClaw can respond normally, enter script generation specific testing.

Terminal input to enter TUI:

openclaw tui

image-20260529193959497

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

  • Help me write a script to make RGB red light blink 5 times, save to desktop and run

  • Generate a script to read temperature and humidity, don't run first

  • Write a script to make servo 1 swing back and forth between 0, 90, 180 degrees

  • Generate a script for OLED to display "Hello OpenClaw"

5. Three Solutions for Communicating with OpenClaw

SolutionSuitable ScenariosAdvantagesRecommended Positioning
whatsappRemote requirement sending, classroom demonstrationsCan directly describe "what script to write for me"Used to demonstrate natural language generation capability
TUILocal debugging, verify generated resultsEasiest to see script generation and run returnsPreferred debugging solution
Voice codeVoice interaction, complete AI experienceCan directly verbally describe script requirementsAs complete demonstration solution

If optional AI voice module + speaker are selected, whatsapp dialogue, tui, whatsapp dialogue need to broadcast OpenClaw's reply content. You can run the following program in terminal. AI voice module dialogue does not need to run repeatedly

  • Modify whether to enable broadcast parameter

xxxxxxxxxx
/home/jetson/voice_to_openclaw/.env
xxxxxxxxxx
ENABLE_TTS=true  #This parameter controls whether to broadcast. After modification and saving, run the following program in terminal
  • Terminal input

xxxxxxxxxx
cd ~/voice_to_openclaw
source .venv/bin/activate
python -m src.openclaw_session_tts

image-20260529194025490

5.1 whatsapp Solution

For whatsapp connection steps, please directly refer to [05-Three Dialogue Solution Configuration Tests]. This section only retains dialogue examples suitable for script generation tutorials.

You can directly dialogue, for example:

  • Generate a script that make servo swing left and right for 10 seconds and run it

image-20260428185931543

5.2 TUI Solution

If you have completed the TUI self-check in Chapter 0, directly enter the following command in the terminal to continue the dialogue:

xxxxxxxxxx
openclaw tui

Then you can directly enter script requirements, for example:

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

    image-20260428190049521

5.3 Voice Code Solution

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 waking up, you can directly say control content, for example:

  • Write a script that displays current temperature and humidity to the screen and run it

image-20260529194118400

6. Comprehensive Cases

Note: The following are all dialogue demonstrations using openclaw tui method. You can also choose whatsapp or voice method to complete.

6.1 Case One: RGB Blinking Script

Experimental Objective

Let 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, it can be run directly or kept for later modification.

Example Instructions

  • Help me write a script to make RGB red light blink 5 times

  • Generate a blue breathing light script, save to desktop and run

  • Write a script for RGB to make a rainbow light effect

6.2 Case Two: Servo Action Script

Experimental Objective

Generate a servo action script to solidify single dialogue actions into repeatable programs.

Effect Description

This type of script is suitable for actions like "nodding", "shaking head", "swinging back and forth", "returning to center", and is also suitable for later expansion into classroom demonstrations or combined projects.

Example Instructions

  • Write a script for servo 1 to reciprocate between 0, 90, 180 degrees

  • Generate a servo nodding script

  • Help me write a script for four servos to all return to 90 degrees

6.3 Case Three: Temperature and Humidity Reading Script

Experimental Objective

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

Effect Description

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

Example Instructions

  • Generate a script to read temperature and humidity, save to desktop

  • Help me write a script to print current temperature and humidity

  • Write a script to read DHT data once and run it

6.4 Case Four: OLED Display Script

Experimental Objective

Generate a script that displays text or indicator content to OLED.

Effect Description

This case is suitable for making welcome pages, status pages, simple dashboards, and is also the foundation for later linking with capabilities like temperature/humidity and cameras.

Example Instructions

  • Help me write a script for OLED to display "Hello OpenClaw"

  • Generate a script to display temperature and humidity to OLED

  • Write a script to display a simple status information page

6.5 Case Five: Combined Script

Experimental Objective

Let OpenClaw generate a comprehensive script containing multiple peripheral actions at once.

Effect Description

This type of script can chain together "reading sensors", "displaying to OLED", "controlling RGB", "driving servos", suitable for transitioning from single peripheral control to small project scripts.

Example Instructions

  • Write a comprehensive script: read temperature and humidity then display to OLED

  • Generate a script: first light green, then turn servo to 90 degrees

  • Help me write a script: if temperature is too high, light red and prompt on OLED

Camera Application
Plant Care Assistant

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