Physical AI
Encord Physical AI Dictionary
Artificial intelligence has spent decades mastering the digital world, recognising images, generating text, and predicting outcomes. Physical AI is the next frontier: systems that don't just process information but act on it, interacting with the real world through sensors, motors, and learned behaviour. For the engineers building these systems and the teams supporting them, precise terminology isn't a nicety; it's the foundation everything else is built on.
What is Physical AI?
Physical AI refers to intelligent systems that perceive their environment through sensors, reason about what they observe, and take physical actions in response. The category spans humanoid robots, autonomous vehicles, drones, and any system where the output isn't a prediction on a screen, but it's a movement in the world.
What unites these systems is the closed loop between perception, reasoning, and action. A wrong prediction in a language model is a nuisance. A wrong movement in a robot can break a component, fail a task, or injure someone. This asymmetry is what makes the data powering Physical AI systems so critical.
How does Physical AI work?
A Physical AI pipeline integrates three core components:
- Perception: Conversion of raw sensor data (RGB video, LiDAR point clouds, depth frames, radar, IMU readings) into a structured representation of the environment. In modern systems, large foundation models serve as the perceptual backbone, encoding scene representations from multi-sensor inputs.
- Reasoning: Interprets what the system has perceived and decides the next steps. Vision-language-action models (VLAs) handle this layer by taking multi-camera video and natural language instructions as input, producing structured action plans or direct motor commands as output. The reasoning layer must handle partial observability, uncertainty, and novel situations not covered by training data.
- Actuation: This translates a decision into a physical command, moving a joint, closing a gripper, rotating a wheel. This must happen at speed and account for the gap between what the model predicts and what the physical system can actually execute.
All three depend on quality of training data. It determines whether a system generalises from controlled lab conditions to real-world variability or fails on contact with anything unfamiliar.
What data does Physical AI require?
Physical AI training data isn't images or text scraped from sources. Its data that is physically collected, precisely synchronised across sensors, and structured to capture not just what the world looks like but what actions were taken in response to it.
Core modalities:
- Multi-view RGB video: Synchronised camera streams from multiple angles
- LiDAR point clouds: 3D spatial data for depth and environment mapping
- RGB-D / depth frames: Geometry at close range, critical for manipulation
- Action labels: What the robot did at each timestep, the target models train against
Natural language captions: Timestamped action descriptions for VLA training
Importance of Data Annotation Quality
Annotation quality is model quality. Raw sensor data can't train a Physical AI system; it has to be labeled. Every frame, point cloud, and trajectory must be annotated with information that tells the model what it's seeing and what the right response is.
For Physical AI, the work is harder than conventional computer vision. A single example can require labeling across multiple simultaneous camera feeds, object tracking across hundreds of frames and more.
A model trained on inconsistent labels learn inconsistent behaviour. In physical systems, data quality directly affects deployment performance.
How Encord Addresses This
Encord is the multimodal data layer built for Physical AI — natively supporting video, LiDAR, RGB-D, and sensor fusion annotation across every stage of the pipeline. Teams use Encord to annotate VLA training data with fine-grained action captions, label multi-view robot demonstrations, run 3D point cloud workflows, and curate datasets with active learning to surface edge cases before they reach training. API/SDK-first. Data stays in your cloud.
Using Encord for Physical AI Data
Encord is the multimodal data layer built for Physical AI, supporting video, LiDAR, and sensor fusion annotation across every stage of the pipeline. Encord can be used to annotate VLA training data with fine-grained action captions, label multi-view robot demonstrations, run 3D point cloud workflows, and curate datasets with active learning to surface edge cases before they reach training.
Explore Encord for Physical AI
Explore Annotation & Labelling
Physical AI Use Cases
Humanoids are being trained on dense multi-view teleoperation data with fine-grained action labels, continuously learning dexterous manipulation in unstructured environments.
Autonomous vehicles and ADAS process synchronised LiDAR, radar, and multi-camera data to detect objects, understand road geometry, and predict road user behaviour in volumes
Industrial and warehouse robotics use Physical AI to handle the variability of real logistics environments, such as irregular objects, dynamic configurations, and human coworkers, that rule-based systems can't manage.
Drones and aerial systems combine LiDAR, RGB, thermal, and multispectral data to navigate autonomously and inspect infrastructure across diverse conditions.
Smart spaces apply Physical AI to fixed sensor networks across retail, construction, and warehouse environments, training systems to monitor activity and respond to events in real time.
Related Terms
Embodied AI · Vision-Language-Action Model (VLA) · Teleoperation · Sim-to-Real Transfer · Trajectory Annotation · LiDAR Annotation · Behaviour Cloning
Related Resources
Informational Guides:
- Accelerating Robotics VLA Segmentation with SAM 3
- Gemini Robotics — Advancing Physical AI with VLA Models
Technical Documentations:
Webinars and video content:
