Short answer
A language model can generate plausible generic procedures cheaply, and robot planners already use language models for task knowledge. Real workplace procedures can add what a general model is unlikely to know: site-specific equipment, parameters, local rules and exceptions. License them when your claim depends on that specificity, and test the difference on a held-out set before deciding.
Why this is a real buying question
Anyone can ask a language model to write a procedure for restocking a shelf or cleaning a hotel room, and the output will usually look reasonable. If that is enough, licensing procedures from operating businesses may add little. If it is not enough, a team that skips real procedures builds on text that sounds right and is not.
The honest answer depends on what the procedure will be used for. This page gives a way to decide using your own tasks. It does not claim that one source is better in general. For the wider question of what any SOP can teach, see what SOPs can and cannot teach a humanoid.
What language models already supply
Robot planning work uses language models as a source of task knowledge. In SayCan, the language model proposes what to do and pretrained robot skills decide what is feasible in context, so that the language model acts as the knowledge and the robot as the hands and eyes. The evaluation was on a mobile manipulator in a real kitchen, and the authors report that grounding roughly halves errors compared with non-grounded baselines (Ichter et al., 2022).
Two implications for a buyer. First, generic task knowledge is already available from the model, so a corpus of generic procedures may add little to a planner of this kind; test it. Second, the gain reported in that work came from grounding in robot capability, not from better procedure text. Procedures help with a different problem: telling the system what this particular business does.
Where real procedures differ
The table lists properties to check on your own sample. They are common concerns, not findings from a specific study, and the size of each difference depends on the task and the generating model.
| Property | LLM-generated procedure | Real workplace procedure |
|---|---|---|
| Generic task structure | Often adequate; check | Present, often mixed with local detail |
| Site equipment and layout | Invented or generic | Named, specific |
| Parameters and tolerances | Plausible but unverified | Set by the business, often with a reason |
| Local rules and exceptions | Absent unless prompted | Present, sometimes undocumented |
| Safety-critical steps | Can be omitted or wrong | Written to meet an obligation; see safety-critical steps |
| Errors | Can be fluent and hard to spot | Outdated or incomplete, but traceable to a source |
| Cost to produce | Low | Depends on supplier, rights and preparation |
| Provenance | Depends on the generating model's terms | Depends on the business and authorship |
Real procedures are not automatically better. They are written for people who already know the job, they drift from practice, and they omit tacit knowledge. See tacit knowledge gaps in SOPs and procedural drift.
Illustrative test on your own tasks
Before licensing, measure whether real procedures change your result.
# Illustrative only - design sketch, values are placeholders
question: "Does adding licensed procedures improve step prediction on held-out sites?"
arms:
A: generic LLM-generated procedures for tasks T1-T10
B: licensed real procedures from sites S1-S4 for the same tasks
C: A and B combined
heldout:
sites: [S5, S6] # never in any arm
annotator: "worker-authored step lists, blind to arm"
metrics:
- step_order_agreement
- parameter_correctness (against site records)
- missing_safety_steps (count)
decision_rule: "license only if B or C beats A on parameter_correctness and
missing_safety_steps by a margin set before the test"
Held-out sites matter. If the test sites appear in the licensed data, the licensed arm will look better by memorizing local detail that will not transfer. The same separation applies to any evaluation set; see procedural reasoning evaluation sets.
When licensing real procedures is likely to matter
- Your robot will work at named sites, and parameters, locations and rules must be right.
- Your evaluation claim is about following a business's own process.
- You need safety-relevant steps taken from documents written to meet obligations.
- You are grounding a planner in a particular organization's vocabulary; see grounding LLM planners with SOPs.
- You want a reference against which to check model-generated plans.
When generated procedures may be enough
- You need broad, generic task structure for early experiments.
- The procedures are scaffolding for annotation, not training targets.
- You are generating variation around a small set of real procedures, with a person reviewing the result.
Generated text used this way still needs checks; validating LLM-extracted steps describes them.
A middle path: real seeds, generated variation
Some teams license a modest set of real procedures and use a model to produce variants, for example rewording steps, changing objects or translating. This can expand coverage of phrasing, but it cannot add facts the seeds lack: a generated variant of a procedure with the wrong torque value repeats the wrong torque value. If you take this path, keep a provenance flag on every record (real, generated, edited), keep real documents separate in any evaluation, and have someone who does the job review a sample of the generated output.
Limitations of both
Neither source contains motion, force or perception data. Real procedures and generated ones are both text, and neither teaches a humanoid physical control. Both can encode human-only assumptions. Generated text can also contaminate an evaluation: if the same model family wrote your test items and your training items, you may be measuring agreement with one model.
Provenance and rights
Generated text is not free of questions. Terms of the generating model may restrict how outputs are used, and a buyer should read them. Real procedures raise ownership and confidentiality questions; see who owns a procedure.
Provenance is hard to verify in both cases. The Data Provenance Initiative audited more than 1,800 text datasets and found that, after re-annotation, 66% of the analyzed Hugging Face licenses fell into a different use category, often labeled more permissive than the author's license; it also reduced "Unspecified" licenses from over 72% to 30% (Longpre et al., 2023). The finding concerns text datasets generally, but it supports a simple rule: record provenance yourself instead of trusting a label. This is not legal advice. The site's guide to licensed vs synthetic vs scraped data covers the broader comparison.
Questions to ask suppliers
- Were the procedures written by people who perform the work, and when were they last revised?
- Are any documents generated or rewritten by software, and if so by which process and with what review?
- Which parts of each procedure are specific to your sites or equipment, and which are generic?
- Do parameters and tolerances come from engineering or quality records, or are they approximate?
- Can you show a few procedures where actual practice deviates from the text?
- Who owns the text, and do any documents incorporate third-party content?
- Can we run the held-out test above on a sample before purchase?
- Are earlier versions kept, so we can see how procedures changed?
- What confidentiality obligations apply to the procedures, and who approves licensing?
Explore sourcing and licensing options
If your tasks depend on how a business really does the work, describe the task domain, document types and permitted uses to SourceX. SourceX looks for US businesses that hold suitable material, checks the data and the supplier's licensing permissions, and coordinates the license and delivery. A request does not guarantee a match; SourceX follows up about potential sources. Start from the humanoid training data guide or the procedures hub, and see the site's page on SOPs and playbooks.
Explore sourcing and licensing options with SourceX
Sources
- The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI — licensing labels in text datasets are often unspecified or mislabeled.
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances — language model task knowledge grounded by robot skills.