Remote Robot Training → Video Analysis → Training Dataset Creation

Set up OBS Studio to capture screen recordings of remote robot control interfaces. Configure multiple scenes to capture both the control interface and robot camera feeds simultaneously. Set recording quality to 1080p at 30fps for optimal training data quality.

Use OpenCV Python scripts to automatically detect action sequences in recorded videos. Implement frame differencing algorithms to identify when robot movements begin and end. Extract key frames at consistent intervals to create a comprehensive dataset.

Upload extracted frames to Roboflow for automated annotation. Use their computer vision models to detect objects, robot positions, and environmental elements. Create bounding boxes and labels for robotic arm positions, target objects, and completion states.

Organize annotated datasets into standardized folder structures with metadata files. Create JSON manifests describing each training sequence, timestamps, and robot actions. Share organized datasets with robotics teams through secure Google Drive folders.