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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。

4. AI Pet

OpenClaw AI Pet Tutorial

Before starting, please complete [01-Peripheral Hardware Configuration], [02-OpenClaw Jetson-KEY Configuration], [03-OpenClaw Model Switching], [04-AI Voice Interaction Configuration], and [05-Three Dialogue Solution Configuration Tests] in the Pre-use Preparation directory. If you are only using TUI / WhatsApp, you can skip 04 for now. This article assumes the basic environment is already prepared and only covers the AI Pet project itself.

1. Tutorial Objective

This article demonstrates OpenClaw's interactive capability as a desktop AI pet through a combination of "movement + lights + screen + dialogue". After completing this tutorial, readers can:

  • Learn how to use servos, RGB, and OLED to create an AI pet with emotional expressions
  • Interact with OpenClaw through three methods: WhatsApp, TUI, and voice code
  • Complete four project scenarios: greeting, emotional expression, companion Q&A, sleep and wake-up

2. Hardware Preparation

  • OpenClaw main control board
  • Servo [at least 1 recommended]
  • RGB light strip / RGB LED module
  • OLED display
  • [Optional] AI voice module + speaker
  • Wiring diagram

image-20260601095714812

3. Software Preparation

Code entry points used in this tutorial

  • Servo control command: /home/jetson/hardware_hal_demo/cli/servo
  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py

This project does not have a fixed set of "pet scripts"; instead, it has OpenClaw orchestrate several basic peripheral actions:

  • Servo is responsible for nodding, shaking head, and swinging
  • RGB is responsible for expressing mood and status
  • OLED is responsible for displaying name, expression, or short text
  • Voice dialogue is responsible for making the pet more like an interactive character

4. Testing Basic Project Capabilities

Before starting this project, please complete an openclaw tui basic dialogue self-check first; if you haven't done so, see [05-Three Dialogue Solution Configuration Tests] first. After passing, proceed to AI Pet project-specific testing.

Enter OpenClaw TUI:

openclaw tui

image-20260601095841135

Then test several basic capabilities:

  • Make the servo nod
  • Set the light to a pink pulsing effect
  • Display "Hello" on the OLED

5. Three Solutions for Communicating with OpenClaw

SolutionSuitable ScenariosAdvantagesRecommended Positioning
WhatsAppRemote interaction, classroom demonstrationsSuitable for demonstrating "remote pet raising"Demonstrate character interaction capability
TUILocal debugging, action verificationEasiest to check if actions and feedback matchFirst run through character action logic
Voice codeFinal interactive experienceMost like a real chatty desktop petUse as final demonstration solution

If you have selected the AI voice module + speaker, Feishu dialogue, tui, and whatsapp dialogue need to broadcast OpenClaw's replies. You can run the following program in the terminal. The AI voice module dialogue does not need to be run repeatedly.

  • Modify the broadcast toggle 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-20260601095921326

5.1 WhatsApp Solution

For WhatsApp integration and bot creation steps, please refer directly to [05-Three Dialogue Solution Configuration Tests]. After creation, you can remotely interact with the AI pet in WhatsApp, for example:

  • Check if there is dog food currently, if so OLED displays a smiley expression, servo dances, OLED lights up ambient lights; if no dog food, OLED displays a sad expression.

image-20260428173743334

5.2 TUI Solution

If you have completed the TUI self-check in the Prerequisites section, directly enter terminal text dialogue:

xxxxxxxxxx
openclaw tui

Then input project-related commands, for example:

  • Check if there is dog food currently, if so OLED displays a smiley expression, servo dances, OLED lights up ambient lights; if no dog food, all servos perform a head-shaking motion simultaneously.

image-20260428173955241

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 refer directly to [04-AI Voice Interaction Configuration]. After wake-up, you can directly speak control commands, for example:

  • Take a photo to check if there is dog food, if so OLED displays a smiley expression, if no dog food OLED displays a sad expression.

image-20260601100303321

6. Project Implementation

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

6.1 Scenario 1: Greeting

Experiment Objective

Have the AI pet respond with movement and text when the user sends a greeting.

Effect Description

The servo can perform a simple nodding or head-shaking movement, the OLED displays welcome text, and the RGB lights up with a soft color, making the pet's entrance quite eye-catching.

Example Commands

  • Have the little pet say hello to me
  • Have the pet welcome me

 

6.2 Scenario 2: Emotional Expression

Experiment Objective

Have the AI pet express states like happy, quiet, angry, and sleepy using lights and screen.

Effect Description

This scenario is best suited for demonstrating "character feel". For example, warm-colored pulsing lights when happy, dim blue light when sleepy, and only brief status display when quiet.

Example Commands

  • Put the pet in a happy state
  • Draw a simple expression on the OLED

6.3 Scenario 3: Companion Q&A

Experiment Objective

Have the AI pet respond to questions like a desktop companion character in addition to moving.

Effect Description

OpenClaw can first complete the dialogue, then combine servo nodding, head shaking, and screen text to synchronize "speaking" and "movement", making it look more like a character with personality.

Example Commands

  • First answer my question, then nod
  • If you agree, express it with movement
  • Chat with me about today's mood

6.4 Scenario 4: Sleep and Wake-Up

Experiment Objective

Give the AI pet the ability to switch between standby, sleep, and wake-up states.

Effect Description

In sleep mode, most lights can be turned off, keeping only a low-brightness state; upon wake-up, lights, screen, and movement feedback are restored, making the project more complete.

Example Commands

  • Put the pet in sleep mode
  • Wake up the pet
AI Rock Paper Scissors Game
AI Weather Station

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