Train Custom Gestures → Generate Training Data → Deploy to Robot Fleet
Create custom gesture training datasets using smartphone recording, process with computer vision AI, and deploy to robotic systems for manufacturing or healthcare applications.
Workflow Steps
iPhone Camera
Record gesture sequences
Mount iPhone with head strap or tripod, record yourself performing target gestures in good lighting with ring light. Capture multiple angles and repetitions of each movement for comprehensive dataset.
OpenCV
Extract motion data
Use OpenCV's pose estimation to analyze recorded videos and extract keypoint coordinates for joints, hands, and body positions. Convert video frames into structured motion data with timestamps.
Roboflow
Augment and label training data
Upload motion sequences to Roboflow, apply data augmentation techniques, and create labeled training datasets. Add metadata tags for gesture types, context, and difficulty levels.
Robot Operating System (ROS)
Deploy to robot controllers
Export trained gesture models and integrate with ROS nodes. Configure robot joints to replicate recorded movements, test in simulation environment, then deploy to physical robots.
Workflow Flow
Step 1
iPhone Camera
Record gesture sequences
Step 2
OpenCV
Extract motion data
Step 3
Roboflow
Augment and label training data
Step 4
Robot Operating System (ROS)
Deploy to robot controllers
Why This Works
Combines accessible smartphone recording with professional computer vision processing, making robot training scalable and cost-effective compared to traditional motion capture systems.
Best For
Training humanoid robots for manufacturing, healthcare, or service applications using crowdsourced human demonstrations
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