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There is no universal dataset size for physical-AI or robot learning. Start with the smallest corpus that covers your task’s success and failure modes, sensor modalities and target environments. Expand only when held-out evaluation reveals a gap. Published projects range from a Microsoft workflow that can begin with one laptop and one robot to industrial collections measured in terabytes and hundreds of thousands of trajectories; those figures are reference points, not minimum requirements.
What “enough data” means for a physical-AI project
A useful dataset lets a model observe the situations it will encounter, choose actions, and recover when conditions differ from the demonstration. That makes coverage more important than a single total such as hours of video or number of episodes.
Define the deployment envelope before collecting: the task objective, robot embodiment, end effector, sensors, workspace, lighting, object set, expected operators and safety constraints. Then list success states and credible failures—occlusion, slips, collisions, calibration drift, unusual object geometry, people entering the scene and interrupted episodes. Your first collection is large enough when each important branch appears in training and in a held-out test set.
Demonstration count should therefore be driven by coverage. A short manipulation task with one object class may need far fewer episodes than a mobile robot operating across sites, but the second task also needs examples of navigation, localization, recovery and changing environments. Add data when evaluation identifies a specific missing condition, not simply because a larger number looks better.
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Published scale examples—and what they do not prove
| Program or release | Reported scale | What is included or claimed |
|---|---|---|
| NVIDIA initial open Physical AI release (March 18, 2025) | 15 TB; more than 320,000 robotics trajectories; up to 1,000 OpenUSD assets | A broad reference release. NVIDIA also says its GR00T models use thousands of hours of video for post-training and that DRIVE AV development requires tens of thousands of driving-data hours. |
| NVIDIA Open-H-Embodiment dataset card (2026) | 750 hours; 120,000 trajectories; 4.5 TB | Paired video and kinematics in LeRobot v2.1 format, with hybrid human, sensor and synthetic collection and labeling; released under CC-BY-4.0. |
| Figure Index announcement (August 25, 2026) | 30 minutes of video uploads every second; more than $1 billion planned for data and compute over the following 12 months | Figure reports 373 unique tasks, 1,146 manipulated objects and 116 environments per 1,000 hours. These are company-reported operational figures, not a recipe for every team. |
| Microsoft documented toolchain | One laptop and one robot for local training | CPU or local-GPU training can begin without cloud infrastructure by default; cloud training is presented as the production path. |
These scales describe particular goals, sensor stacks and organizations. They cannot be converted into a universal minimum number of demonstrations. A small, well-controlled proof of concept may be scientifically valid, while a general-purpose foundation model can require many terabytes and extensive post-training video.
What to collect in every episode
Physical-AI data is more than camera footage. Collect the signals that let a learner connect perception, state and action in time.
Synchronized sensor and robot streams
Record video together with robot state and action: joint positions and velocities, gripper commands, force or torque where available, and timestamps from a common clock. Mark episode start, termination, pauses and resets. Without alignment, a frame can be paired with the wrong action, creating label noise that is difficult to diagnose later.
Calibration and spatial registration
Store camera intrinsics and extrinsics, robot-to-camera transforms and the calibration procedure or version. If multiple cameras, LiDAR or depth sensors are used, record their coordinate frames and synchronization offsets. Sensor-synchronized, spatially calibrated collection is also how commercial labeling services describe LiDAR, three-dimensional bounding-box and semantic work.
Task outcomes and recovery events
Mark the intended goal, whether it was achieved, partial progress, failure type and any recovery action. A failed grasp, a collision stop and an operator intervention are different training signals. Keep the original trajectory rather than deleting every failure; filter only when privacy, safety or corruption requires removal.
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Geometry and semantics when the policy needs them
Add depth, segmentation masks, three-dimensional boxes, object poses and contact-relevant attributes when the learning objective depends on them. Imitation policies often gain more from correctly aligned actions and episode outcomes than from exhaustive prose descriptions on every frame. Perception models, by contrast, may require modality-specific boxes, masks, poses or depth labels.
Provenance and quality metadata
For each episode, retain robot and embodiment, environment or site, operator, software and firmware versions, consent or license status, sensor configuration and known anomalies. This metadata lets you find distribution shifts, remove a faulty firmware run and demonstrate that data can legally be used for the intended deployment.
How much labeling is enough?
Label the information required by the objective, then audit its accuracy and coverage. Labeling every frame with every possible attribute is expensive and can distract from the signals a policy actually uses.
- Imitation learning: prioritize action/state alignment, episode boundaries, task identity and success or failure outcomes.
- Perception: add boxes, masks, object poses, depth or semantic classes at the spatial and temporal resolution needed by the model.
- Manipulation and contact: capture gripper state, force or torque, contact events and object pose when they influence success.
- Safety and recovery: annotate emergency stops, near misses, human interventions and the recovery policy that followed.
Use automatic or sensor-derived labels where they are reliable, but sample them for human review. Open-H-Embodiment explicitly combines human, automatic or sensor and synthetic collection and labeling; its video is stored as MP4, kinematics as Parquet and manifests as JSON or JSONL. That separation makes it possible to validate each modality independently.
Production curation is part of labeling economics. Figure describes filtering, fraud review, deduplication, rebalancing and annotation in its pipeline. A lower-volume dataset with duplicate, corrupted or misaligned episodes can be less useful than a smaller, audited set.
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Diversity versus volume: what to prioritize
More hours help only when they add useful variation. NVIDIA quoted Wei Zhan, codirector of Berkeley DeepDrive, saying that “Data diversity is incredibly important to train foundation models.” For a task-specific policy, diversity should be tied to the situations the robot must handle.
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- Objects: include shape, size, texture, mass, friction, damage and packaging changes that affect grasping or placement.
- Embodiment and sensors: cover the robot versions, camera placements, lenses, depth units and grippers that will run the policy.
- Operators and motion style: use multiple demonstrators when human technique is part of the expected variation.
- Failure modes: deliberately collect slips, occlusions, blocked paths, partial successes and safe recoveries.
- Geography and sites: separate locations and hold out at least one meaningful environment for evaluation instead of mixing all frames randomly.
Track the real-to-synthetic mix and the risks of simulation-to-reality transfer. Synthetic scenes can efficiently expand geometry, lighting and rare events, but they do not automatically reproduce contact physics, sensor artifacts or human behavior. Validate synthetic gains on real, held-out episodes.
A practical method for sizing your first collection
- Write the task contract. Specify inputs, action space, success test, safety limits, robot configuration and environments the model must support.
- Map the coverage matrix. List object and environment variants, sensor conditions, operators, normal trajectories and failure or recovery branches. Mark which combinations are safety-critical.
- Run a pilot collection. Capture enough episodes to exercise every listed branch at least once and to expose synchronization, calibration and storage problems. Do not treat the pilot as a final training set.
- Build a held-out evaluation set. Keep episodes, environments or objects out of training according to the deployment question. A random frame split can hide memorization because adjacent frames are nearly identical.
- Measure task-level performance. Report success, recovery, collision or intervention rates and latency on the robot. NIST emphasizes that metrics should reflect the combined effects of the algorithm, robot system and task, not model accuracy alone.
- Expand by the largest gap. If failures cluster around glare, a particular object, a second robot arm or a recovery event, collect that slice rather than adding unrelated hours.
- Freeze versions and repeat. Record dataset, calibration, software and model versions so each improvement can be attributed to a change in data, labeling or training.
This loop gives a defensible answer to “How many demonstrations?”: stop when additional episodes no longer close an evaluation gap or improve the safety and reliability target you set.
When a laptop is enough—and when to use cloud GPUs
Compute planning covers preprocessing, storage and egress, experiment tracking, evaluation and inference latency as well as gradient updates. Microsoft’s documented progression provides a useful operational model.
| Stage | Typical trigger | Operational needs |
|---|---|---|
| T0: local | One robot, early experiments and a modest corpus | One laptop using CPU or a local GPU; no cloud infrastructure by default. |
| T1: shared lab | One machine or one person becomes a scheduling bottleneck | A shared GPU box, access controls, repeatable environments and basic dataset governance. |
| T2: cloud pilot | Experiments need larger GPUs, parallel runs or remote access | Microsoft lists cloud training as the recommended production path. Its example A100 virtual machine rate is approximately $3.06 per hour. Azure subscription roles, GPU quota and an NVIDIA NGC account are required for that example. |
| T3–T4: fleet scale | Multiple sites, robots or frequent retraining | Centralized data delivery, job scheduling, monitoring, evaluation dashboards and retraining analytics. |
| T5: fleet intelligence | Fully integrated fleet learning and operations | Documented by Microsoft as roadmap-only on the cited page, not an established capability to budget as available. |
A laptop is usually adequate for data-format validation, small imitation-learning runs, calibration checks and policy debugging. Move to shared or cloud GPUs when preprocessing and training delay iteration, several experiments must run concurrently, or access, quota and audit requirements exceed what a personal machine can manage.
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Storage, throughput and deployment costs
Estimate storage from an actual sample of your sensor configuration rather than from a generic “hours of video” figure. Measure bytes per episode for each camera, depth stream, LiDAR scan, robot-state file and metadata manifest, then multiply by planned episodes, retained failed runs and versioned derivatives. Include replication, backups, preprocessing scratch space and cloud egress.
Choose formats that preserve timing and allow partial reads. Open-H-Embodiment’s MP4 video, Parquet kinematics and JSON or JSONL manifests illustrate a practical separation. Store checksums and schema versions so a corrupted or changed file cannot silently enter training.
Deployment can impose a tighter constraint than training. Measure end-to-end inference latency, sensor capture delay, control frequency and network reliability on the target robot. A larger model that improves offline accuracy but misses the control deadline is not an improvement in the physical system.
Common dataset mistakes and fixes
Counting frames instead of independent experiences
Thousands of adjacent frames may represent one nearly identical motion. Count episodes, objects, environments and failure branches, and design evaluation splits that prevent near-duplicate leakage.
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Dropping every failure
Removing unsuccessful attempts can produce a brittle policy that has never seen recovery conditions. Keep safe failures with outcome and intervention labels; remove only unsafe, corrupted or unauthorized data.
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Ignoring calibration drift
Recalibrate after camera or robot changes and attach calibration versions to episodes. A policy can appear to regress when the real change is a shifted coordinate frame.
Mixing embodiments without identifiers
Different arm lengths, grippers or joint conventions require explicit embodiment metadata and, often, normalization or separate policy heads. Do not merge streams that the model cannot distinguish.
Budgeting only GPU-hours
Human review, deduplication, storage, data transfer, evaluation runs and on-robot tests can dominate a project. Include them in the schedule and cost model from the pilot onward.
Assuming synthetic data removes real-world collection
Simulation is valuable for rare events and scalable variation, but contact dynamics, sensor noise and human interaction still need real validation. Keep a real-world holdout and report its results separately.
Decision checklist
- Can every training episode be aligned to the correct state and action timestamps?
- Are camera and other sensor frames calibrated and versioned?
- Do labels identify success, failure and recovery rather than only demonstration intent?
- Does the collection cover the environments, objects, embodiments and failure modes that matter?
- Is a held-out, task-level evaluation set protected from near-duplicate leakage?
- Have licensing, privacy, consent and provenance been verified for commercial use?
- Are preprocessing, storage, egress, review and evaluation included alongside GPU cost?
- Can the deployed robot meet the required inference latency and safety limits?
Bottom line
Start small, but make the first dataset structurally complete: synchronized streams, calibration, action and outcome labels, provenance and representative failures. Use held-out robot evaluations to decide what to collect next. Local training is sufficient for the first iterations; shared and cloud GPUs become justified when iteration speed, governance or fleet scale demands them. The largest published datasets demonstrate what industrial foundation-model programs consume—not a threshold your project must reach.
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