Physical AI Training Data Requirements: What to Collect Before You Scale

Useful physical AI training data connects observations, instructions and actions to an actual learning objective. For robotics teams, the current challenge is not just collecting more video: it is specifying the supervision, diversity, quality and usage rights that make data usable for the intended model.

Field Motion performs large-scale collection across egocentric, stereo, MONO and UMI formats. Each can contribute to a training program, but no capture format removes the need to define the target task and robot. The checklist below turns a request for “robotics training data” into a concrete collection brief.

Start with the learning stage, not an hours target

Pretraining, policy adaptation and evaluation need different datasets. Broad human activity video can help a model learn visual and task representations. Action-conditioned policy learning needs supervision compatible with the intended action space. Evaluation needs held-out episodes or robot trials that test whether the policy works outside its training examples.

EgoScale describes human-video pretraining followed by aligned human–robot mid-training. The OpenPI release provides another reference for adapting pretrained robot models. These recipes support thinking in stages; they do not establish a universal number of demonstrations for every task.

Seven requirements for a collection brief

1. Observations that match the intended model

Specify what the learner receives: first-person RGB, external views, stereo pairs, depth estimates, robot state or combinations of these. Record camera placement, image dimensions and frame rates. If training observations differ from deployment sensors, make the adaptation plan explicit.

MONO can provide a single visual stream. Stereo adds a paired-camera geometry requirement. Egocentric specifies the participant's viewpoint. UMI adds a manipulation collection interface. See our capture-format guide for their distinctions.

2. A precise action definition

Document whether actions are joint positions, end-effector poses, pose deltas, gripper states or something else. Include coordinate frames, units, control frequency and the relationship between observations and action timestamps. A pose trajectory is not a force measurement; an action label is not necessarily the command sent to a robot.

For human footage without robot actions, specify the actual labels available and the intended transfer method. For UMI, validate the trajectory and gripper outputs against the policy's expected representation rather than assuming compatibility.

3. Temporal alignment and calibration

State which clock timestamps refer to, how streams are synchronized and how dropped frames are handled. Stereo projects also need intrinsics, distortion parameters and the camera-to-camera transform. Retain calibration versions so a downstream team can determine which records apply to each episode.

4. Task, language and outcome labels

Write task instructions that distinguish the object, action and goal. Define episode boundaries and success criteria. If failures or recoveries are collected, label them separately: a failed attempt can be useful for some training objectives, but is not automatically a positive imitation example.

5. Planned environmental diversity

Track coverage across object instances, layouts, lighting, participants and execution styles. Decide which variations matter for the deployment setting. Use the DROID dataset as a research example of in-the-wild manipulation collection, not a shortcut to assuming your program has comparable coverage.

6. Data provenance and permitted uses

Keep collection permissions, participant consent, source identifiers and agreed training uses with the dataset. Identify privacy-sensitive content before delivery. Document redistribution and commercial-use terms; a public download or research paper does not by itself establish that every downstream use is permitted.

7. Acceptance checks and dataset versioning

Agree on checks for corrupt files, missing fields, timestamp discontinuities, unusable views and failed tracking. Separate raw capture from accepted episodes and report rejection reasons. Version the manifest, schema and annotations so the training team can reproduce a run.

Keep training and evaluation meaningfully separate

A random frame split can leak nearly identical observations from the same episode into both training and evaluation. For sequential manipulation data, consider held-out episodes, objects, locations or participants according to the generalization claim you intend to measure.

Define a robot evaluation protocol alongside the data brief: task success, intervention criteria and test conditions should be explicit. Better offline metrics are useful evidence, but they are not a substitute for deployment-relevant robot trials.

A practical pilot before production collection

• Choose a representative task and the proposed capture configuration.
• Collect a small batch with the full labels, metadata and permissions workflow.
• Load it through the real preprocessing and training path.
• Review coverage, rejection reasons and any policy evaluation results.
• Revise the specification before expanding participants, environments or volume.

This pilot can expose incompatible action conventions or missing metadata early. It cannot guarantee that scaling the same recipe will solve every task; the purpose is to remove avoidable integration errors before committing to a larger operation.

Frequently asked questions

How much data does a physical AI model need?

There is no universal count. The answer depends on pretraining, action space, task complexity, data quality and deployment variation. Use a pilot and learning curves to guide expansion; see our guide to demonstration counts.

Is more footage always better?

No. Repeated footage, missing labels or incompatible observations can increase storage without improving the learner. Measure accepted coverage and downstream results alongside raw hours.

What should I send Field Motion?

Send your task list, intended robot or model, preferred capture formats, required labels and delivery requirements to info@fieldmotion.ai. Field Motion can scope large-scale collection, annotation and development against that brief.