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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。

7. Scheduled Tasks

OpenClaw Scheduled Tasks Tutorial

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

1. Tutorial Objectives

This article demonstrates how OpenClaw upgrades from instant dialogue to automated peripheral applications through AI-generated scripts with built-in loops or OpenClaw cron scheduling. After completing this tutorial, readers will be able to:

  • Understand the basic implementation approach for OpenClaw scheduled tasks
  • Distinguish the suitable scenarios for built-in loops, HEARTBEAT, and cron
  • Describe automation requirements through three methods: WhatsApp, TUI, and voice code

2. Hardware Preparation

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

Note:

  • The core of this tutorial is "scheduled execution", not requiring all peripherals to be connected simultaneously
  • Connect the corresponding hardware first depending on which type of automation task you want to perform

3. Testing Scheduled Task Basic Capabilities

Before starting this project, please complete a basic openclaw tui dialogue self-check; if you haven't done so yet, see [05-Three Dialogue Solutions Configuration Test] first. After passing, proceed to the scheduled task specific test.

It is recommended to test in the following order:

  1. First enter OpenClaw TUI
  2. Have OpenClaw generate a simple script
  3. Manually run the script in the terminal to confirm it works correctly

Enter in terminal:

openclaw tui

It is recommended to first try prompts like:

  • Help me write a script to display current temperature and humidity on OLED, save to desktop
  • Generate a script that reads temperature and humidity and prints the result each time it runs
  • Write a script to make RGB light up green for 3 seconds then turn off

After the script is generated, run it manually first, for example:

xxxxxxxxxx
python3 /home/jetson/Desktop/your_script.py

3.1 Task Type Selection Rules

Scheduled tasks are handled in two categories:

  • Seconds-level / High-frequency tasks: For example, "read temperature and humidity every 5 seconds", "continuously refresh OLED", "constantly monitor sensors". Generate independent Python/Bash scripts, use while True + sleep loops inside the script, and run in the background.
  • Minutes-level / Hours-level tasks: For example, "remind every minute", "sample temperature and humidity every hour", "execute at fixed time daily". Suitable for using OpenClaw cron.

Simple rule: Use script loops for short-interval hardware polling; use cron for tasks with intervals of one minute or longer.

4. Three Solutions for Communicating with OpenClaw

SolutionSuitable ScenariosAdvantagesRecommended Use
WhatsAppRemote automation requirement descriptionSuitable for first clarifying "what to do on a schedule"Use for generating scripts and confirming requirements
TUILocal creation and debugging of scheduled tasksEasiest for joint debugging of scripts and OpenClaw cronPreferred implementation solution
Voice codeVoice description of automation scenariosCloser to desktop assistant experienceSuitable for demonstrating "generate automation by saying one sentence"

If the optional AI voice module + speaker is selected, Feishu dialogue, TUI, and WhatsApp dialogue need to broadcast OpenClaw's reply content. You can run the following program in the terminal. The AI voice module dialogue does not need to run it repeatedly.

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

image-20260529194211404

4.1 WhatsApp Solution

For WhatsApp integration steps, please refer directly to [05-Three Dialogue Solutions Configuration Test]. This section is more suitable for describing requirements, for example:

  • Help me create a scheduled task: make RGB light up red every minute as a reminder, then turn off

image-20260428191042352

4.2 TUI Solution

If you have completed the TUI self-check in article 0, directly enter terminal text dialogue:

xxxxxxxxxx
openclaw tui

Then directly enter project-related commands, for example:

  • Help me create a scheduled task: read temperature and humidity every minute and display on OLED

image-20260428191030929

4.3 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:

  • Help me create a scheduled task: read temperature and humidity every minute and display on OLED

image-20260529194236118

  • If you want to stop the scheduled task, you can say "help me stop the current scheduled task" in the current conversation session, or follow the instructions to delete the task in the terminal using kill pid

    image-20260511194439572

  • You can enter cron list in the openclaw tui dialogue to view current OpenClaw scheduled tasks; if viewing in a regular Linux terminal, use openclaw cron list

image-20260511194516325

5. Project Implementation

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

5.1 Difference Between Second-level Tasks and Cron

If the requirement is second-level looping, for example:

  • Read temperature and humidity every 5 seconds and display on OLED
  • Check environment status every 10 seconds
  • Continuously refresh screen or light status

These tasks should have OpenClaw generate a background script that loops internally, for example while True with time.sleep(5), and return the script path, process PID, log path, and stop command.

If the requirement is minute-level, hour-level, or fixed-time triggering, for example:

  • Flash RGB reminder every minute
  • Sample temperature and humidity every hour
  • Run an inspection at a fixed time each day

These tasks should use OpenClaw cron.

5.2 Scenario 1: Hourly Temperature and Humidity Sampling

Experiment Objective

Have the device automatically read temperature and humidity once per hour, and record the results or sync to the screen.

Effect Description

This is the most basic and common scheduled task scenario, which can later be extended to logging, dashboards, environmental alerts, or plant care projects.

Example Commands

  • Help me create a scheduled task: read temperature and humidity every minute and display on OLED

5.3 Scenario 2: Timed Status Reminder

Experiment Objective

Use RGB or OLED to provide a clear reminder at a specific time.

Effect Description

This type of task is suitable for water drinking reminders, class reminders, inspection reminders, and status switching reminders. It does not require continuous running, but rather triggers a clear action at a specific moment.

Example Commands

  • Help me create a scheduled task: make RGB light up red every minute as a reminder, then turn off

5.4 Scenario 3: Timed Inspection Script

Experiment Objective

Chain multiple peripheral actions together to form a small automation task that can be executed periodically.

Effect Description

For example, read temperature and humidity once per hour, if the status is abnormal turn on a light reminder, and display the results on OLED. This type of task is already close to a standalone small project, and is the point where "AI-generated scripts" and "scheduled execution" are most visibly combined.

Example Commands

  • Help me create a scheduled task: read temperature and humidity once per minute, turn on red light when abnormal, and show a prompt on OLED
Temperature and Humidity Dashboard

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