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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThere is no universal dataset size for physical-AI or robot models. Start with the smallest corpus that represents your task’s success and failure modes, sensor setup, robot embodiment and target environments. Expand only when held-out evaluation exposes a gap. Public reference points range from one robot trained locally to 15 TB and more than 320,000 trajectories, but those large figures are examples of scale, not minimum requirements.
What determines how much physical-AI data you need?
A trajectory is one recorded attempt at a task, usually including observations, robot state, actions and an outcome. A useful dataset is therefore measured by coverage, not just hours or terabytes. A thousand nearly identical successful demonstrations can leave a policy unable to recover from a dropped object, a reflective surface or a slightly different shelf height.
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Estimate your initial corpus against five questions:
- What must the robot do? Define the task boundary, acceptable precision, speed and safety constraints.
- How can it fail? List object variation, occlusion, collisions, sensor dropouts, human interference and recovery situations that matter in deployment.
- Which signals are available at run time? A model cannot rely on labels or sensors that will not exist on the production robot.
- Where will it operate? Include the lighting, layouts, surfaces, weather, geography and other environmental changes the system must tolerate.
- What evidence will decide whether it is ready? Keep held-out environments, objects and operators for evaluation instead of using every episode for training.
Use evaluation gaps to set the next collection target. If failures cluster around a particular object pose, camera angle or recovery action, collect those cases rather than adding more routine demonstrations.
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Published datasets show the range, not a required minimum
Large public and commercial programs illustrate how quickly storage and throughput grow when the objective is a general-purpose model. Their numbers should not be interpreted as a threshold for a focused robot project.
| Program | Reported scale | What the figure represents |
|---|---|---|
| NVIDIA initial open Physical AI release (2025) | 15 TB; more than 320,000 robotics trajectories; up to 1,000 OpenUSD assets | A broad release combining robotics data and assets for Physical AI development |
| NVIDIA Open-H-Embodiment dataset (2026) | 750 hours; 120,000 trajectories; 4.5 TB | Paired video and kinematics in LeRobot v2.1 format, with human, sensor and synthetic collection and labeling |
| Figure Index announcement (August 25, 2026) | 30 minutes of video uploads per second; more than $1 billion planned for data and compute over 12 months | Company-reported operational investment and ingestion rate; per 1,000 hours, 373 unique tasks, 1,146 manipulated objects and 116 environments |
These programs optimize for breadth across many robots, tasks and environments. A single manipulation cell, warehouse route or inspection procedure can reach useful performance with orders of magnitude less data if its operating envelope is narrow and well controlled.
Build a minimum viable dataset before collecting at scale
1. Specify the deployment envelope
Write down the robot model and end effector, camera and other sensors, control frequency, workspace, object set, expected operators and environmental range. Record which variables are fixed and which will change. This prevents collecting impressive-looking demonstrations that do not match production hardware.
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2. Capture complete episodes
Store synchronized video together with robot state and actions. Every episode needs reliable timestamps, a clear start and end boundary, and an outcome such as success, failure, timeout or human intervention. Include the action and state immediately before a failure; those transitions are often more useful for learning recovery than another successful run.
3. Reserve evaluation data
Split by environment, object instance, operator or day when possible, not only by adjacent frames. A random frame split can put nearly identical views in both training and test sets and make performance look better than it is. Keep a final set of unseen combinations for release decisions.
4. Expand from observed gaps
After a baseline, group errors by cause: perception, reachability, grasp choice, timing, contact dynamics or recovery. Collect targeted episodes for the largest error groups. This active approach usually improves a policy faster than uniformly increasing the number of routine demonstrations.
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What should be recorded and labeled?
Synchronized sensor and robot streams
Synchronize cameras, depth or LiDAR where used, joint positions and velocities, gripper state, actions, force or torque signals, and any external tracking. Preserve timestamps at capture time and document dropped frames. Video without aligned actions cannot support imitation learning, and actions without the corresponding observation cannot explain why a control decision was made.
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Calibration and spatial registration
Keep camera intrinsics, camera-to-robot extrinsics, robot geometry and the calibration procedure used. Recalibrate after a camera mount moves and version the calibration with the episode. Spatially misregistered sensors can create labels that are internally consistent but physically wrong. Sensor-synchronized, spatially calibrated collection is also important for LiDAR, 3D boxes and semantic labels.
Task and outcome labels
At minimum, identify the task, demonstration or policy source, episode boundaries, success or failure, and any recovery event. For a failed attempt, mark whether the robot, object, environment or operator caused the failure when that distinction is observable. These labels support filtering, curriculum design and honest evaluation without requiring a sentence of annotation for every video frame.
Geometry and semantics when the objective requires them
Add depth, segmentation masks, 3D bounding boxes, object poses, contact points or material and friction attributes only when the model or downstream metric uses them. A grasp planner may need precise object pose and gripper contact labels; a behavior-cloning policy may primarily need aligned actions and outcomes. Labeling every possible property increases cost and can delay useful iterations.
Provenance and quality metadata
Record the robot embodiment, environment, operator, software and firmware versions, sensor configuration, consent or license status, collection date and known failure modes. Open-H-Embodiment stores video as MP4, kinematics as Parquet and manifests as JSON or JSONL; adopting similarly explicit formats makes later filtering and auditing practical.
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How much labeling is enough?
Label the information required by the learning objective, then spend effort auditing fidelity and coverage. For imitation learning, accurate action/state alignment and episode outcomes generally deliver more value than exhaustive frame-by-frame prose. For perception models, modality-specific labels such as boxes, masks, poses or depth become necessary.
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Quality review should check timestamp drift, missing frames, impossible joint values, calibration errors, duplicate episodes, mislabeled outcomes and demonstrations that violate safety constraints. Figure’s reported production pipeline—filtering, fraud review, deduplication, rebalancing and annotation—shows that curation is part of labeling economics, not an optional cleanup step at the end.
Use double review or adjudication for safety-critical labels and sample audits for high-volume automatic labels. Keep the original sensor data alongside corrected labels so that a later audit can distinguish a capture problem from an annotation change.
Is diverse data better than simply more data?
For physical AI, diversity often matters more than raw hours once the common cases are covered. Vary the dimensions that change the physics or the visual evidence:
- Objects: shape, size, mass, texture, deformability, damage and clutter.
- Environments: layouts, surfaces, lighting, weather, floor friction, shelves and work heights.
- Embodiments: arms, grippers, joint limits, camera placement and control interfaces.
- People and operation: operators, hand positions, intervention styles and safe-recovery behavior.
- Outcomes: normal success, partial success, slips, collisions, occlusions, sensor faults and deliberate recovery attempts.
Do not add variation blindly. If production uses one calibrated camera and one gripper, collecting many unrelated embodiments may dilute the training signal. Match diversity to the deployment distribution, then add out-of-distribution cases that a safety policy must detect or handle.
NVIDIA quoted Berkeley DeepDrive codirector Wei Zhan on March 18, 2025: “Data diversity is incredibly important to train foundation models.” The practical implication is to measure diversity explicitly—unique objects, environments, task variants and failure categories—instead of reporting only total hours.
When can you train locally, and when do you need cloud GPUs?
Compute should scale with iteration speed, dataset throughput, experiment count and deployment requirements. Microsoft documents a progression that starts with one laptop and one robot and moves toward shared and cloud infrastructure.
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| Stage | Use it when | Operational requirements |
|---|---|---|
| T0: local | One robot, one developer and a modest dataset are sufficient for rapid experiments | CPU or local GPU training; no cloud infrastructure by default |
| T1: shared lab | A single machine or person is blocking iteration, or several users need the same data and models | Shared GPU box, access controls, dataset versioning and basic experiment governance |
| T2: cloud pilot | Training duration, storage, reproducibility or remote collaboration exceeds local capacity | Microsoft lists cloud training as the recommended production path; its example A100 virtual machine rate is approximately $3.06 per hour, with an Azure subscription, appropriate roles, GPU quota and an NVIDIA NGC account |
| T3-T4: fleet scale | Multiple sites or robots require centralized delivery, monitoring and recurring retraining | Managed data pipelines, release controls, fleet telemetry and retraining analytics |
| T5 | Long-term fleet intelligence planning | Documented as roadmap-only on the cited Microsoft page rather than an established operating tier |
The A100 price is an example hourly VM rate, not a complete project budget. Add preprocessing, storage, data transfer or egress, checkpoint retention, experiment tracking, evaluation runs and inference hardware. A training job that uses a GPU for ten hours may require many more CPU hours to decode video and build batches.
Plan storage and data throughput before the first large capture
Estimate storage from camera count, resolution, frame rate, compression, episode duration and retention policy. Keep a lossless or minimally compressed source for safety-critical signals, and generate derived training clips or resized frames rather than repeatedly decoding the original archive. Store manifests that point to immutable files so a model can be reproduced after labels change.
Separate hot data used in current experiments from cold archival data. Test upload and download throughput with a representative batch; a cloud GPU that waits on video transfers is not cheaper in practice. Track checksums, schema versions and software versions for every exported dataset.
Evaluate the complete robot system, not just the model
NIST frames the pipeline as data collection, preprocessing, training and deployment. Its guidance emphasizes metrics that reflect the combined effects of the algorithm, robot system and task. Report task success, recovery success, collision or near-miss rate, intervention frequency, latency and performance on held-out environments. For navigation or manipulation, include time-to-completion and energy or compute limits when they affect deployment.
Measure inference latency on the target computer and sensors. A policy that scores well offline but misses the control deadline can fail physically. Re-run the same evaluation after calibration changes, firmware updates and data rebalancing; those changes can alter behavior without changing model weights.
Common dataset mistakes and how to correct them
Collecting only successful demonstrations
Symptom: the robot performs a nominal action but cannot recover from a small disturbance. Correction: add supervised failures, human recoveries and safe interruption states, then evaluate recovery separately from nominal success.
Ignoring synchronization and calibration
Symptom: labels appear plausible, yet grasps consistently miss or contact timing is wrong. Correction: verify timestamp alignment and camera-to-robot registration with calibration targets and known motion tests before collecting more episodes.
Letting near-duplicates dominate
Symptom: validation accuracy is high but performance drops in a new room or on a new object. Correction: deduplicate repeated views, split evaluation by environment or object instance, and rebalance underrepresented conditions.
Using synthetic data without measuring the sim-to-real gap
Symptom: a model trained on simulation fails on real textures, contact dynamics or sensor noise. Correction: mix synthetic and real episodes, randomize only measured sources of variation, and maintain a real-world holdout for every critical task.
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Symptom: a technically useful dataset cannot be shared or deployed commercially. Correction: record consent, license, provenance and geographic restrictions at capture time, and verify partner-program terms before commercial reuse.
A practical starting plan for a new robot project
- Define one task and its safety envelope. Write the success metric, failure taxonomy, sensors and target environments.
- Capture a pilot set. Record synchronized video, state, actions, timestamps, calibration and outcomes across operators and representative conditions.
- Build a clean held-out set. Reserve unseen objects, layouts or collection days before training.
- Train locally. Use a laptop or local GPU while the pipeline, labels and evaluation are changing quickly.
- Inspect failures on the robot. Categorize errors and verify whether they arise from data, perception, control, latency or hardware.
- Collect targeted additions. Prioritize the largest evaluation gaps and rare but safety-critical failures.
- Move to shared or cloud GPUs when justified. Trigger the move when local iteration, storage, governance or fleet size becomes the bottleneck, not merely because a larger dataset is fashionable.
- Version and audit every release. Preserve manifests, calibration, software versions, licenses and metrics so a deployed model can be traced back to its data.
That loop produces a defensible answer to “how much data?” for your robot: enough episodes to cover the measured task envelope, plus targeted diversity wherever evaluation still fails.
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