Physical AI Training Data: Real-World Datasets for Models That Understand Physics
Physical AI systems — robots, world models, embodied agents — cannot learn physics from text or static images. They need video of the real world: objects falling, hands grasping, tools operating, people navigating. Claru provides this data at scale.
What Is Physical AI and Why Does It Need Different Data?
Physical AI is artificial intelligence that operates in the physical world. It includes any system that must understand three-dimensional space, predict how objects move and interact, or take physical actions based on sensory input. This is fundamentally different from language models (which process token sequences) or image classifiers (which label static photographs).
A physical AI system solving a simple task — picking up a cup from a cluttered table — must understand depth (how far away is the cup), geometry (what shape is the cup, where is the handle), physics (how heavy is it, will it tip if grasped from the side), semantics (that is a cup, not a bowl), and dynamics (the cup will move when I push it, the liquid will slosh). This multi-layered understanding cannot be learned from internet text or stock photography.
Physical AI requires training data that captures how the real world works: video showing physical interactions, depth information revealing 3D structure, segmentation maps identifying object boundaries, pose estimates tracking how hands and bodies move, and action labels describing what is happening and when. This data must come from diverse real-world environments, not just simulation, because no simulator perfectly reproduces the visual and physical complexity of reality.
The Physical AI Stack: Where Training Data Fits
Physical AI is not a single model — it is a stack of capabilities, each requiring distinct data.
Perception
The system must build a 3D understanding of its environment from raw sensor input. This includes depth estimation, object detection and segmentation, scene reconstruction, and spatial relationship reasoning. Training perception requires video with ground-truth depth, segmentation masks, and 3D bounding boxes.
World Modeling
A world model predicts how the scene will change — what happens if I push this object? Where will it land? World models are trained on video sequences showing physical interactions, learning the implicit physics of the environment. They need diverse footage of objects being manipulated, dropped, stacked, poured, and rearranged.
Policy Learning
The policy maps observations to actions — given what I see, what should I do? Policies are trained via imitation learning (mimicking demonstrations) or reinforcement learning (optimizing a reward signal). Both require paired observation-action data: what the robot saw and what it did, synchronized at millisecond precision.
Language Grounding
Modern physical AI systems accept natural language instructions. Grounding language to physical actions requires data where natural language descriptions are paired with the corresponding physical demonstrations. This enables systems like RT-2 and Octo to follow instructions like 'pick up the red cup and place it on the shelf.'
Why Synthetic Data Alone Is Not Enough for Physical AI
Simulation has made remarkable progress. Engines like NVIDIA IsaacSim, MuJoCo, and PyBullet can render photorealistic scenes and simulate rigid body physics at real-time speeds. For many teams, synthetic data is the starting point for pre-training.
But simulation has fundamental limitations that create a persistent gap between simulated and real-world performance:
- •Visual distribution mismatch. Simulated textures, lighting, and materials do not fully capture the variability of real environments. A robot trained on simulated kitchens will encounter real countertops, reflective surfaces, transparent objects, and clutter patterns that no simulator has modeled.
- •Physics approximation. Real-world contact dynamics — friction, deformation, compliance, granular materials, liquids — are approximated in simulation, not reproduced. A policy that works perfectly in MuJoCo may fail when the real object is slightly heavier, more slippery, or more compliant than the simulated version.
- •Long-tail scenarios. Real environments contain an effectively infinite variety of objects, arrangements, and disturbances. Simulation can model known variations, but it cannot anticipate every real-world surprise. A child's toy on the floor, a wet surface, an unexpected reflection — these edge cases determine whether a deployed system works or fails.
- •Sensor noise and calibration. Real cameras have lens distortion, motion blur, rolling shutter artifacts, and varying exposure. Real depth sensors have noise patterns that differ from simulated depth. Training on clean synthetic data produces policies that are brittle to sensor imperfections.
The current standard approach is pre-train on synthetic data, then fine-tune on real-world data. The synthetic data provides task structure and volume. The real-world data provides the visual and physical fidelity needed for deployment. Claru provides the real-world side of this equation — the data that cannot be generated in a simulator.
Claru's End-to-End Data Pipeline for Physical AI
From raw video capture through multi-layer enrichment to delivery in your training pipeline's native format.
- •Wearable cameras (GoPro, smartphones)
- •Managed teleoperation
- •Game-based capture
- •10,000+ contributors
- •100+ cities worldwide
- •Monocular depth estimation
- •Semantic + instance segmentation
- •Human + hand pose estimation
- •Optical flow computation
- •AI-generated captions
- •Action boundary labels
- •Object affordance tags
- •Quality scoring (blur, occlusion)
- •Domain-specific metadata
- •Cross-annotator validation
- •WebDataset, HDF5, Parquet, RLDS
- •S3 or GCS delivery
- •Datasheet + methodology docs
- •Checksums + manifests
- •Custom format support
Every stage of the pipeline is designed for the specific requirements of physical AI research. Capture protocols ensure consistent camera perspectives and sufficient temporal resolution (minimum 30 FPS, 60 FPS for fast-motion tasks). Enrichment models are selected and validated for the robotics domain — our depth estimation pipeline is calibrated against LiDAR ground truth where available. Annotation guidelines are developed in collaboration with each client's ML team to ensure labels match the exact format and granularity their training code expects.
Physical AI Data at Scale
The Physical AI Landscape
Physical AI has moved from research papers to funded companies with deployment timelines. Here is where the field stands.
Humanoid robotics has attracted billions in venture capital. Multiple companies have demonstrated bipedal platforms performing warehouse tasks, household chores, and factory operations. These systems universally rely on imitation learning from human demonstrations — and they are all data-constrained.
World models are emerging as a foundational capability. Learned simulators trained on video data can predict future states of physical scenes, enabling planning without explicit physics engines. Video generation companies and robotics labs are converging on world models as a shared technical layer — and both need the same input: diverse video of physical interactions.
Vision-language-action (VLA) models represent the current frontier of robot policy architectures. These models combine pre-trained vision-language backbones with action prediction heads, enabling robots to follow natural language instructions. VLA models are more data-efficient than previous approaches but still require tens of thousands of real-world demonstrations for robust deployment.
Autonomous systems beyond robotics — self-driving vehicles, drones, agricultural equipment — face the same data challenge: they need diverse real-world observations to handle the long tail of scenarios that simulation cannot cover.
Across all of these verticals, the pattern is the same. The algorithms have converged on learned policies. The hardware is advancing rapidly. The binding constraint is data — specifically, real-world data that captures the diversity and complexity of physical environments.
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Frequently Asked Questions
What is physical AI?
Physical AI refers to artificial intelligence systems that understand and interact with the physical world. Unlike language models or image classifiers that operate on digital inputs, physical AI systems must reason about gravity, friction, object permanence, spatial relationships, and cause-and-effect in three-dimensional space. Physical AI encompasses robotics (manipulation, locomotion, navigation), world models (learned simulators that predict how physical scenes evolve), embodied agents (systems that perceive and act in real environments), and autonomous vehicles. The common thread is that these systems must understand physics — not from equations, but from observations of how the real world actually works.
Why does physical AI need different training data than other AI systems?
Physical AI models must learn representations of 3D space, object physics, temporal dynamics, and action consequences — none of which are captured in static images or text. A language model learns from token sequences. An image classifier learns from labeled photographs. A physical AI system needs video with depth, segmentation, and pose annotations; demonstration trajectories with action labels; multi-view observations of the same scene; and temporal sequences long enough to capture cause-and-effect relationships. The data must also be embodiment-specific: a dataset collected from a ceiling-mounted camera is not useful for training a wrist-mounted camera policy. Physical AI training data is fundamentally multi-modal, temporally structured, and grounded in specific physical contexts.
What is a world model and what data does it need?
A world model is a learned simulator that predicts how a physical scene will evolve over time. Given a current observation and an action (or no action), a world model outputs a predicted future observation. World models are trained on large volumes of video showing physical interactions: objects falling, sliding, colliding, being manipulated, and deforming. The training data needs to cover diverse physical phenomena — rigid body dynamics, soft body deformation, fluid behavior, articulated object motion — across many environments and lighting conditions. Claru's egocentric video datasets provide this diversity: 500,000+ clips from kitchens, workshops, warehouses, and outdoor environments, each enriched with depth maps, segmentation masks, and optical flow to provide the supervision signals world models require.
Can synthetic data replace real-world data for physical AI?
Synthetic data from simulators like IsaacSim, MuJoCo, or Habitat is valuable for pre-training and for learning task structure, but it cannot fully replace real-world data for physical AI. The fundamental issue is the sim-to-real gap: simulated environments do not perfectly reproduce real-world physics (contact dynamics, deformable objects, friction models), visual appearance (lighting, textures, material properties), or environmental diversity (clutter patterns, object arrangements, background variation). Policies trained exclusively on synthetic data typically experience 30-60% performance degradation when deployed on real hardware. The most effective approach combines synthetic pre-training with real-world fine-tuning. Claru provides the real-world data component — the data that bridges the gap between simulation and deployment.
How does Claru's data pipeline work for physical AI?
Claru's pipeline has four stages. Capture: raw video and sensor data are collected through wearable cameras (10,000+ contributors), managed teleoperation (client-specific robot hardware), or game-based capture (custom environments logging synchronized video and inputs). Enrich: automated models process every clip — monocular depth estimation, semantic segmentation, instance segmentation, human/hand pose estimation, optical flow, and AI-generated captions. All enrichment layers are cross-validated for consistency. Annotate: human annotators add task-specific labels including action boundaries, object affordances, quality scores, and domain-specific metadata. Deliver: datasets are packaged in standard ML formats (WebDataset, Parquet, HDF5, RLDS) with datasheets documenting methodology and intended use.
What companies are building physical AI?
Physical AI is being pursued across several verticals. In robotics: companies building manipulation systems (warehouse picking, assembly, food preparation), humanoid robots (bipedal platforms for general-purpose tasks), and mobile robots (delivery, inspection, agriculture). In world models: research labs and startups training learned simulators from video data for planning and prediction. In autonomous vehicles: self-driving car and truck companies that need to understand physical scene dynamics. In embodied AI research: academic and industrial labs building agents that can perceive, reason about, and act in physical environments. Claru works with frontier labs across these verticals, though we do not disclose specific client names.
What annotation layers are most important for physical AI?
The most critical annotation layers for physical AI training data are: depth maps (providing 3D spatial understanding from 2D observations), semantic and instance segmentation (identifying every object and its boundaries), human and hand pose estimation (for manipulation and interaction understanding), action labels (temporal boundaries of discrete actions with verb-noun descriptions), optical flow (dense motion fields capturing inter-frame dynamics), and object affordance annotations (which parts of objects can be grasped, pushed, or operated). Claru provides all of these layers through a combination of automated enrichment models and human annotation, with cross-validation between layers to ensure consistency.
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