A comprehensive interface for controlling Vector robots through Wirepod local HTTP APIs and CLI scripts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install vector-control
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install vector-control using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Vector Control?
The Vector Control skill provides a bridge between your automation environment and the Vector robot hardware. By utilizing Wirepod local HTTP endpoints, this skill enables programmatic access to the robot's physical components, including its drivetrain, head, lift, and camera. It is designed for developers who want to integrate robotics into their wider ecosystem using Openclaw Skills.
This skill eliminates the complexity of raw SDK management by providing a streamlined CLI helper. Whether you are building a custom security patrol or a robotic assistant, the Vector Control skill offers the necessary primitives to move, speak, and see through the eyes of Vector without relying on cloud services.
Vector Control Use Cases
- Automating home security through scheduled camera snapshots and patrolling.
- Creating interactive voice responses using Vector's text-to-speech capabilities.
- Developing autonomous exploration routines to map or monitor local environments.
- Integrating robotic physical movement into smart home triggers and alerts.
- Remote teleoperation of hardware via a simple CLI interface within Openclaw Skills.
How Vector Control Works
- The skill identifies the target robot using its unique Electronic Serial Number (ESN) stored in the Wirepod configuration.
- A control session is initiated via the assume command, which grants the skill priority over the robot's internal behaviors.
- Commands for movement, speech, or media playback are sent as HTTP requests to the Wirepod API SDK endpoints.
- Real-time camera data is retrieved through a MJPG stream and can be processed into standard image formats.
- Once tasks are complete, the control session is released to return the robot to its default autonomous state.
Vector Control Setup
To begin using this skill with Openclaw Skills, ensure your Vector is connected to a Wirepod instance on your local network.
# Find your ESN in the Wirepod config
cat /etc/wire-pod/wire-pod/jdocs/botSdkInfo.json
# Assume control of the robot
python3 skills/vector-control/scripts/vector_control.py --serial <ESN> assume
# Execute a test speech command
python3 skills/vector-control/scripts/vector_control.py --serial <ESN> say --text "Openclaw Skills activated"
Vector Control Data Schema & Taxonomy
The skill manages interaction data and media through the following schema:
| Component | Data Type | Usage |
|---|---|---|
| ESN | String | Unique identifier for the robot hardware |
| Wheel Speed | Integer (0-200) | Determines velocity of left and right treads |
| Camera Feed | MJPG / JPEG | Raw stream from /cam-stream endpoint |
| Audio | WAV (8kHz Mono) | Internal format for robot speaker playback |
| Routine | JSON/CLI Args | Parameters for patrol or exploration logic |
Vector Control Advanced Features
- Multi-step patrol routines with deterministic sweep patterns for efficient area coverage.
- Randomized exploration mode that allows Vector to wander while performing specific tasks.
- Automatic audio transcoding that converts MP3 or WAV files to the robot's native 8kHz mono format.
- Integration with FFmpeg for high-speed frame extraction from the robot's camera stream.
- Scripted 'Assume/Release' lifecycle management to prevent conflicts with manual robot control in Openclaw Skills.
SKILL.md
Loading
Related Openclaw Skills
Railway Deploy
Automate code deployments to Railway using the CLI with support for custom messages, service selection, and real-time log streaming.
Academic Writer & LaTeX Composer
A professional LaTeX writing assistant that automates the lifecycle of academic paper creation from template analysis to PDF compilation.
Beaver Habit Tracker
An AI-powered integration for the Beaver Habit Tracker API that automates habit logging and visualization through an intelligent agent interface.
Volcengine TOS Storage Management
A comprehensive Python-based integration for managing Volcengine Tinder Object Storage (TOS) buckets and object lifecycles through AI agents.
Veryfi Documents AI
Veryfi Documents AI is a real-time OCR and data extraction engine designed to transform unstructured documents into structured, actionable JSON data.
PDF To Structured Data Extraction
Automate the extraction of structured data from complex construction PDFs, specifications, and Bill of Materials using advanced OCR and parsing.
Featured*
