A specialized bridge that translates 2D vision detection into 3D spatial understanding and robotic action sequences within a self-evolving loop.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install diepre-embodied-bridge
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 diepre-embodied-bridge 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 DiePre Embodied Bridge?
DiePre Embodied Bridge is a technical implementation of the vision-action-evolution-loop, specifically engineered to bridge the gap between 2D dieline detection and 3D robotic planning. Unlike generic 3D reconstruction methods like NeRF or Gaussian Splatting which require heavy GPU resources, this skill utilizes known geometry estimation and FEFCO standards to perform rapid calculations on standard hardware. It is a core component for developers looking to integrate Openclaw Skills into industrial packaging and robotics workflows.
By leveraging the Model Context Protocol (MCP), the skill wraps complex OpenCV and geometry pipelines into callable tools. This allows Vision-Language-Action (VLA) models to interact with physical objects through a structured API, managing everything from dieline analysis to grasp point computation with high confidence and low latency.
DiePre Embodied Bridge Use Cases
- Automating the conversion of 2D CAD/DXF files into 3D folding sequences for industrial packaging.
- Calculating high-precision robotic grasp points based on material thickness and surface normals.
- Implementing self-optimizing assembly lines that learn from mechanical failures like grasp slips.
- Real-time quality inspection and defect detection in automated folding environments using Openclaw Skills.
How DiePre Embodied Bridge Works
- The vision system captures 2D imagery or DXF dielines which are analyzed to identify FEFCO packaging types.
- The geometry engine calculates 3D coordinates and fold matrices using standardized rules rather than intensive 3D reconstruction.
- A toolchain generates a fold sequence and identifies optimal grasp points (XYZ, force, and angle) for the robotic arm.
- Every task execution is logged, capturing input parameters, timing, and success/failure data.
- An evolution scanner periodically analyzes these logs to identify failure patterns and update a set of evolved parameters for future tasks.
DiePre Embodied Bridge Setup
To install this skill, use the following command in your terminal:
clawhub install diepre-embodied-bridge
Alternatively, for manual installation within your Openclaw Skills directory:
cp -r skills/diepre-embodied-bridge ~/.openclaw/skills/
DiePre Embodied Bridge Data Schema & Taxonomy
| Data Type | Format | Description |
|---|---|---|
| Vision Input | JPG/PNG | Source image for dieline and FEFCO identification |
| Geometry Output | DXF | Parametric 2D/3D layout data |
| Evolution Log | JSON | Record of task ID, failure steps, and execution timing |
| Parameters | JSON | Evolved values for grasp force and fold overrides |
DiePre Embodied Bridge Advanced Features
- MCP (Model Context Protocol) integration for seamless tool-calling by VLA models.
- Self-evolving feedback loop that automatically tunes robotic force and angles based on historical success rates.
- High-efficiency CPU-based 3D estimation optimized for M1 Max and similar architectures.
- Parametric fold sequence planning that supports custom FEFCO rule overrides within Openclaw Skills.
SKILL.md
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