| name | Edge Deployment Skill |
| description | ML model optimization and deployment on robot edge devices (Jetson, embedded) |
| slug | edge-deployment |
| category | Deployment |
| allowed-tools | ["Bash","Read","Write","Edit","Glob","Grep"] |
| graph | {"domains":["domain:robotics"],"specializations":["specialization:robotics-simulation"],"skillAreas":["skill-area:motion-planning","skill-area:sensor-fusion"],"roles":["role:research-engineer"]} |
Edge Deployment Skill
Overview
Expert skill for optimizing and deploying machine learning models on robot edge devices including NVIDIA Jetson and embedded systems.
Capabilities
- Configure TensorRT optimization for NVIDIA Jetson
- Set up ONNX model conversion and optimization
- Implement INT8 and FP16 quantization
- Configure DeepStream for video analytics
- Set up CUDA graph optimization
- Implement model pruning and distillation
- Configure DLA (Deep Learning Accelerator) deployment
- Set up multi-stream inference
- Implement ROS2 inference nodes
- Profile and benchmark on target hardware
Target Processes
- nn-model-optimization.js
- object-detection-pipeline.js
- rl-robot-control.js
- field-testing-validation.js
Dependencies
- TensorRT
- ONNX Runtime
- NVIDIA Jetson SDK
- DeepStream
Usage Context
This skill is invoked when processes require deploying ML models on edge devices with optimized inference performance.
Output Artifacts
- TensorRT engine files
- ONNX optimized models
- Quantization configurations
- DeepStream pipeline configs
- Inference benchmark reports
- ROS2 inference node implementations
