AI Data Glossary: Key Terms Every Robotics Buyer Should Know

The AI data industry has developed its own dense vocabulary, and it's easy for buyers new to physical AI to lose track of what a vendor is actually offering. This glossary covers the terms that come up most often when scoping a data collection or annotation program.

Data Preparation

• Data Collection — sourcing raw video, images, text, audio, or sensor data from the real world
• Data Curation — filtering and cleaning raw data, removing noise, duplicates, and personal identifying information
• Data Augmentation — modifying existing data (flipping images, adding audio noise) to expand a dataset's effective diversity
• Synthetic Data Generation — using AI or 3D engines to artificially generate training data rather than capturing it from the real world

Annotation and Labeling

• Bounding Box — a rectangular or 3D region marking an object's location in an image or video frame
• Semantic / Instance Segmentation — pixel-level outlines of object boundaries
• Pose Estimation / Keypoint Annotation — coordinate tracking of joints, fingers, or facial features
• Ground Truth — the verified, human-labeled reference data that model predictions are scored against

Human Feedback and Alignment

• Human-in-the-Loop (HITL) — a workflow where humans continuously review and correct AI outputs during training
• RLHF — Reinforcement Learning from Human Feedback, where humans rank AI responses to train a reward model
• Red Teaming — deliberately trying to break or trick an AI to find safety flaws before release

Learning Paradigms

• Supervised Learning — training on fully labeled input/output pairs
• Self-Supervised Learning — a model learns by predicting parts of its own input, without external labels
• Imitation Learning (Behavioral Cloning) — training a robot to perform a task by copying human demonstrations
• Active Learning — a workflow where the model flags its own uncertain examples for human labeling

Evaluation

• Inter-Annotator Agreement (IAA) — a measure of how consistently different annotators label the same data
• Sim-to-Real Transfer — testing whether a simulation-trained model performs successfully in the physical world

Where Blue Projects Fits In

Blue Projects works across most of these categories in practice — field data collection, annotation, and structured delivery for robotics and physical AI clients. If a term on this list describes what you need, it's likely something we can scope directly.

Frequently Asked Questions