Robotics Training Data

Robotics training data consists of visual, sensor, demonstration, and outcome information tailored to a model’s intended task, with object detection, segmentation, keypoint localization, and imitation learning each requiring different labels and recordings. The guide outlines a Roboflow-based workflow for building visual perception datasets from egocentric robot video, emphasizing camera views that match deployment, consistent recording metadata, removal of near-duplicate frames, and data splits organized by recording session to prevent overly optimistic evaluation. In its example, images of a metal block are labeled both with bounding boxes and a 2D grasp-point keypoint, then used to train separate RF-DETR detection and keypoint models. Rather than prescribing a fixed dataset size, it recommends collecting diverse examples covering expected lighting, positions, orientations, backgrounds, occlusions, and failure conditions. Dataset versioning supports reproducible comparisons between training rounds, while held-out recordings and manual prediction review help identify meaningful failures. After deployment, perception predictions can be monitored alongside task outcomes, with failed cases investigated across the full robotics pipeline before new data is collected, labeled, retrained, and evaluated.

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