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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。

1. Servo Control

OpenClaw Servo 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 servo-related content.

1. Tutorial Objectives

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

  • Understand the basic approach of OpenClaw controlling servos

  • Chat with OpenClaw through three methods: WhatsApp, TUI, and voice interaction

  • Complete four servo examples: single servo control, servo dancing, yes/no Q&A, simulated table tennis, simulated pointer

     

2. Hardware Preparation

  • OpenClaw main control board
  • Servo [1 / 4 units]
  • [Optional] Microphone module
  • Wiring diagram

3. Software Preparation

Code entry points used in this tutorial

  • Low-level driver code: /home/jetson/hardware_hal_demo/
  • Python driver library: /home/jetson/Downloads/python/
  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py
  • Terminal text entry: /home/jetson/voice_to_openclaw/src/text_main.py

4. Testing the Servo

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 servo-specific test.

Enter the following command in the terminal to enter TUI:

openclaw tui

image-20260529190710332

After ensuring the hardware is connected, you can test servo control by entering the following in the terminal:

  • Turn servo 1 to 0 degrees
  • Turn the servo to 90 degrees
  • Turn the servo to 180 degrees

Note: If possible, specify which servo number to control

image-20260424183345752

If you can see the servo driving normally, it means all of the following are working properly:

  • OpenClaw can receive commands
  • Servo power supply is normal
  • Servo angle control range is correct
  • Clear feedback is visible after command execution

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

image-20260529190729858

5.1 whatsapp Method

For WhatsApp connection, QR code creation, and gateway restart steps, please refer to [05-Three Chat Modes Configuration Testing]. This section only retains chat examples suitable for the servo tutorial.

You can chat directly, for example:

  • Turn servo 1 to 0 degrees
  • Turn the servo to 90 degrees
  • Make the servo swing left and right

107ff2282b6e022cdd999b244f6ff6d4

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-20260529190802382

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]. Voice mode is more suitable for demonstrating spoken servo commands, for example:

  • Turn the servo to 90 degrees
  • Make the servo nod
  • Make the servo swing left and right

image-20260529190838111

6. Comprehensive Cases

Note: The following are all demonstrated using openclaw tui for chat (you can choose your preferred chat method)

6.1 Case 1: Servo Dancing

Experimental Objective

Make the servo move continuously according to a set of preset angles, creating a rhythmic "dancing" effect.

Effect Description

The servo can switch between multiple angles, such as swinging left, centering, swinging right, rapid shaking, and then returning to the initial position. Even with just one servo, the changes in movement rhythm can still create a noticeable "dancing feel".

Example Commands

  • Make the servo dance for 10 seconds
  • Make the servo swing left and right, with a faster rhythm
  • Arrange a three-beat dance step for the servo

6.2 Case 2: Yes/No Q&A

Experimental Objective

Make the servo express "yes" or "no" with movements, such as nodding for "yes" and shaking for "no".

Effect Description

When OpenClaw determines the answer is affirmative, the servo performs a nodding action; when the answer is negative, the servo performs a head-shaking action. This case is very suitable for highlighting the combination of "natural language understanding + action expression".

Example Commands

  • If the answer is "yes", make the servo nod
  • If the answer is "no", make the servo shake its head
  • Help me judge this question and answer with servo movements

6.3 Case 3: Simulated Table Tennis

Experimental Objective

Make the servo continuously move back and forth, simulating the feeling of a ping pong ball bouncing back and forth across the table.

Effect Description

The servo oscillates at a constant or variable speed between the left and right boundary angles. If accompanied by voice descriptions or screen prompts, the audience will easily understand this as a "ping pong ball back and forth" simulation.

Example Commands

  • Make the servo simulate a ping pong ball bouncing back and forth
  • Swing left and right 20 times, with a faster speed
  • Start from the left, do a back and forth movement demo

6.4 Case 4: Simulated Pointer

Experimental Objective

Use the servo as a rotatable pointer to indicate values, directions, or states.

Effect Description

This case can very intuitively demonstrate the mapping from "abstract values -> actual angles". For example, mapping values from 0 to 100 to 0 to 180 degrees, the servo can point to the target position like a gauge needle.

Example Commands

  • Turn the pointer to 30%
  • Make the servo point to the 90 degree position
  • Simulate a dashboard, the current value is 75
Three Dialogue Configuration Tests
RGB Strip

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