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About this report
Physical AI for Humanoids examines the software, data and operating responsibilities between a promising robot demonstration and a maintainable commercial deployment. It follows the development and operating loops from demonstrations and simulation through adaptation, held-out evaluation, onboard inference and controlled fleet releases. The central buying question is what a particular configuration can deliver on a defined task, with what human assistance, rights and support obligations.
The report profiles nine configurations spanning NVIDIA GR00T and Isaac tooling, the Gemini Robotics family, openpi, LeRobot with SmolVLA, OpenVLA, Octo and Figure’s integrated Helix 02 stack. It compares access and integration responsibilities rather than creating a misleading performance league table. Buyer diligence sections and six fictional decision laboratories explore acceptance denominators, transfer to a new site, targeted data collection, release trade-offs, portability and remote-assistance capacity.
Download contents: The paid download is a ZIP package containing the 54-page full report PDF, the 8-page preview PDF and 5 CSV files covering platform comparisons, benchmark-claim worksheets, due-diligence questions and claim/source ledgers. It also includes read-me, methodology/review-status and third-party-notice text files. No XLSX workbook is included. CSV files are static tables, not automatically recalculating workbooks. The separate free download remains the preview PDF only.
Executive summary
A model checkpoint or reasoning interface is one part of a robot service, not proof of dependable work. The report distinguishes the rights attached to code, weights, datasets, simulator assets and operational recordings, then connects those distinctions to integration and continuity. It treats a customer announcement, an inspectable artifact and a provider benchmark as different forms of evidence, each useful for a different decision and none automatically establishing customer-specific autonomy or economics.
The proposed evaluation sequence begins with a bounded task and a rights inventory, establishes a baseline and held-out test, verifies the deployed configuration, and measures failures and recovery alongside accepted output. Configuration identity, intervention accounting, controlled updates and practical export rights are recurring diligence priorities. Development effort, human assistance and switching costs remain visible rather than disappearing behind an inference-time figure or an open-source label.
Who this report is for
Technical buyers and AI leaders evaluating humanoid software platforms.
Robotics and machine-learning teams responsible for data, adaptation and integration.
Operations, procurement and security teams assessing support, rights and continuity.
Coverage
Learning architecture
episode collection, demonstrations, teleoperation, simulation, synthetic data, adaptation and held-out evaluation.
Platform configurations
GR00T and Isaac; Gemini VLA, ER and On-Device; openpi; LeRobot/SmolVLA; OpenVLA; Octo; and Helix 02.
Commercial control
artifact-level rights, site-data reuse, model access, portability, vendor dependencies and integration responsibilities.
Operating evidence
end-to-end latency, fleet configuration, assistance demand, release governance, recovery and cost per accepted task.
Key questions
- Which parts of the proposed stack can be inspected, modified, retained or exported, and under which artifact-specific terms?
- What exact robot, model, runtime, task distribution and intervention policy produced the cited result?
- How much integration, data preparation and adaptation work separates the available model from the intended task?
- How will updates, failures, remote assistance and human authority be governed across the operating fleet?
- What remains usable if the provider changes its service, retires a version or the customer changes vendors?
Table of contents
Cover and contents
Page 1-3Executive judgment, scope, method and interests
Page 4-7Reference architecture, data collection, teleoperation and simulation
Page 7-14Training, claim evaluation, onboard inference and fleet operations
Page 14-19Nine platform configurations and the comparison matrix
Page 20-28Data and model rights, integration, switching costs and economics
Page 29-33Cybersecurity, recovery, human authority and customer evidence
Page 34-38Staged evaluation, accountable-owner questions, claim worksheet and decision synthesis
Page 39-45Six decision laboratories: acceptance, transfer, data, release, portability and staffing
Page 45-52Source register and publisher methodology
Page 52-54
Methodology
The report reviews selected official repositories, model cards, license documents, provider documentation and customer announcements available at the 16 October 2026 evidence cutoff. Its source register contains 22 references. Mutable repositories and web pages are point-in-time observations, not permanent release or access guarantees.
Evidence is separated into documented artifacts, provider claims, customer statements and original editorial analysis. Nine configurations were chosen to expose different architectures, access models and integration responsibilities, not to represent the entire market or support an aggregate performance ranking. Missing documentation remains an evidence gap, not proof that a capability or agreement does not exist.
The architecture diagrams, diligence framework and six decision laboratories are original analysis. Numerical cases are explicitly fictional and illustrate decisions rather than vendor quotations, operating forecasts or measured field outcomes. No model checkpoint, robot or production line was operated for the report.
Generative AI assisted research synthesis, drafting, analysis, calculations and graphics. Source comparisons, computational cross-checks, navigation checks and visual reviews were internal production checks, not external professional review, certification or an independent audit. No hands-on robot field tests or interviews were conducted.
Disclosures
This educational research is not a hands-on evaluation, security audit, legal opinion, engineering design approval, safety certification or personalized investment advice. It does not establish deployment approval, license clearance, financial returns or suitability for a particular application. Qualified specialists must assess the actual facts, intended use and jurisdiction.
One Robotics Supply Group LLC publishes under Humanoid Robotics Reports. The publisher’s owner has ownership interests in related businesses, including robotics supply and distribution. As of 17 October 2026, the owner reports no investments in other companies covered by the collection and no current coverage-related sponsorship, dealer or referral connections. These statements are owner-reported, not independently audited. Related commercial interests can influence coverage; any future material coverage-related relationship, compensation or benefit requires a specific, current disclosure.
Company and customer statements remain attributed claims. The report does not independently reproduce platform performance, reconstruct every repository revision, or review private contracts. Later production checks do not refresh every underlying fact. Buyers should obtain current artifact licenses, configuration records and customer-specific acceptance evidence before relying on a purchasing conclusion.
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