Private AI Robotics Prototyping: Sovereign Stack Guide

Posted: May 2, 2026 to Robotics.

Robotics is the next frontier where artificial intelligence stops being a chat partner and starts being an actuator. The same shift, however, is colliding with the regulatory reality that most of the interesting robotics research is funded by defense, healthcare, and federally-sponsored academic programs whose data plane cannot legally cross the boundary of a third-party software-as-a-service vendor. This guide is for the principal investigators, research engineers, lab directors, and program managers who are trying to figure out how to build modern AI-driven robots without putting their grant, their accreditation, or their export license at risk.

Petronella Technology Group has been operating in cybersecurity, compliance, and private AI for 23 years. Our robotics practice is new in 2026, and we are honest about that. The infrastructure underneath it, the GPU fleet, the secure network architecture, the CMMC Registered Provider Organization status, and the digital forensics depth, is operational and verifiable today, and the Reachy Mini sitting on a desk in our Raleigh lab is one we own and run.

The reader we wrote this for usually shows up in one of three shapes. The first is a defense principal investigator with a freshly-awarded SBIR or DARPA contract that mentions DFARS flow-down, an Other Transaction Authority agreement, or covered defense information. The second is a research-university lab lead whose grant has a data management plan attached and whose Institutional Review Board has just signed off on a human-subjects protocol that involves video capture. The third is a healthcare R&D engineer whose hospital legal team has handed them a Business Associate Agreement and a thirty-day deadline to demonstrate where the data plane will live. The architectural questions are similar across all three, and the answers map back to the same handful of frameworks. The differences sit in which framework owns the audit trail, which one pays the consequences if something goes wrong, and which one the lab has to staff against. We touch each of those threads in the sections below, and the FAQ at the end answers the questions that come up after the first read.

Why cloud LLMs are a non-starter for regulated robotics R&D

The default reflex for any team building an AI agent in 2026 is to call a hosted application programming interface. That reflex is fine for marketing copy, customer support, and code-completion tooling. It is dangerous for robotics R&D in regulated environments, and the reasons are not theoretical.

Hosted LLM endpoints retain prompts and completions for various periods, route inference traffic through unspecified geographies, and offer business associate or government-cloud variants only at higher tiers and only for some surfaces of their stack. Even when those higher tiers exist, they rarely cover the full life cycle of a robotics project. A robot policy is trained on sensor data, manipulation traces, and demonstration recordings that often capture protected health information, controlled technical data, or proprietary design content. Sending that data to a public endpoint to fine-tune a model is the same problem as sending it to a contractor without a signed agreement.

The relevant frameworks are explicit. NIST SP 800-171 r3 requires Controlled Unclassified Information to be processed inside a defined system boundary with documented connections, monitored flows, and limited external service use. DFARS 252.204-7012 flows down to every subcontractor handling covered defense information, including the firm running your training jobs. The CMMC Final Rule codifies the assessment regime that proves you actually do all of the above. HIPAA imposes its own boundary, audit, and breach-response obligations. ITAR and the Export Administration Regulations add territorial limits on who can even read certain robot designs.

None of those frameworks were written with cloud foundation models in mind, but they all apply. The shortest path through the audit is to keep the data on your premises in the first place, which is what sovereign robotics prototyping is for.

It is worth being precise about a class of failure mode that comes up repeatedly in conversations with research groups. A vendor will offer a "private endpoint" backed by a familiar foundation model, point at a SOC 2 Type II report, and suggest that the combination satisfies the regulation in question. SOC 2 is a service-organization controls report, not a CMMC, HIPAA, or ITAR conformance document. It is a useful signal, and it is not a substitute. The contract language a regulated lab actually needs covers data residency, sub-processor disclosure, breach notification timelines, and the right to audit. Most public-cloud LLM contracts do not cover all four of those at the tier most labs can afford, and several of them explicitly exclude one or more in the standard terms. Reading the actual contract before signing is the cheapest piece of due diligence in the entire project, and it is the one most often skipped because the marketing page looked reassuring.

Background: private AI fundamentals, GPU economics, LeRobot ecosystem

Private AI is the practice of running training, fine-tuning, and inference on hardware you control, inside a network you control, with weights you own. It is not a new concept. What changed between 2023 and 2026 is that the cost of doing it well dropped to a level where research labs and mid-sized firms can plausibly stand it up without writing a paper-size check, and the open-source ecosystem caught up with the closed alternatives for many real-world tasks.

On the compute side, the relevant economic question is no longer "can we afford to own GPUs" but "which GPU class fits which workload." A behaviour cloning experiment on a Reachy Mini may train comfortably on a single RTX PRO workstation. A multi-task vision-language-action policy needs an HGX node with eight high-bandwidth GPUs. A foundation-model-scale run on tens of millions of trajectories needs DGX BasePOD territory. The right answer is rarely "the biggest box" because robotics datasets are smaller and more curated than internet-scale text. The wrong answer is "rent it from a public cloud" the moment any of the data is regulated.

On the software side, the Hugging Face LeRobot project has emerged as the de facto open-source stack for affordable robotics machine learning. The library crossed twelve thousand stars by April 2025 according to the Hugging Face acquisition announcement of Pollen Robotics, supports a growing list of real-world robot platforms including the SO-101, Koch v1.1, LeKiwi, Hope Jr, Reachy 2, Unitree G1, and Earth Rover Mini, and currently lists thirty-nine LeRobot models and one hundred eighty-one LeRobot datasets on the LeRobot Hugging Face organization page. Required versions per the official installation docs are Python 3.12 or newer and PyTorch 2.10 or newer.

The Reachy Mini is the desktop humanoid acquired by Hugging Face along with Pollen Robotics in April 2025. The launch announcement on the Hugging Face Reachy Mini blog post documents the form factor at twenty-eight centimetres tall in active mode, sixteen centimetres wide, and 1.5 kilograms, with six head degrees of freedom plus full body rotation plus two animated antennas, one wide-angle camera, four microphones, and a five-watt speaker. The wireless variant adds an inertial measurement unit and a Raspberry Pi 4 onboard. Pricing is published at $299 for the Lite variant and $449 for the wireless variant, with bulk-order contact at Petronella Technology Group at our contact page.

The combination matters. The robot is cheap enough that a research group can buy several. The software stack is open source and runs end to end on a self-hosted Linux plus GPU machine, so a private deployment does not require a special variant of the library. The result is a credible path to a regulated robotics R&D environment that does not depend on any external SaaS endpoint at runtime.

GPU fleet sizing for robotics training

The first question every regulated robotics project should answer is "how much compute do we actually need," and the second is "what is the right way to acquire it." Petronella Technology Group sources NVIDIA DGX, HGX, and RTX PRO compute through the NVIDIA Elite Partner Channel and operates the gear inside our private network rather than reselling cloud capacity, which lets us keep training inside the sovereignty boundary the regulation cares about.

Three classes of NVIDIA platform cover the practical robotics range. RTX PRO workstations and small servers fit single-node behaviour cloning, simulation-only experiments, and the inference side of any policy that has been trained somewhere else. They are quiet enough to live in a lab and cheap enough that a research group can buy two or three. HGX server platforms scale to eight high-bandwidth GPUs in a single node and fit multi-task vision-language-action training, sim-to-real with large replay buffers, and small-scale foundation model fine-tuning. DGX systems are the right answer for foundation-model-scale runs, multi-node training, and consortium-scale shared infrastructure.

The decision is not just about throughput. Each platform class has a different power, cooling, and physical-security profile, and each maps to a different operating model. RTX PRO nodes sit in a normal data closet on a normal twenty amp circuit. HGX needs three-phase power and active cooling. DGX systems usually need a dedicated room or a colocation footprint. For CMMC Level 2 work, all three need to live inside the assessment boundary, which means physical access controls, video surveillance, and badge logging that match the controls in NIST SP 800-171 r3.

The right way to size is to start from the dataset, not the brochure. A typical Reachy Mini behaviour cloning task with a few hundred episodes of teleoperation data trains in single-digit hours on a single RTX PRO node. Adding a vision-language-action head pushes that to overnight on an HGX node. Pretraining a small world model on synthetic trajectories for sim-to-real may take days on multi-GPU HGX. None of that requires DGX-class compute unless the project is targeting humanoid foundation-model territory, at which point the conversation usually becomes a multi-institution consortium rather than a single lab build.

On-premises LeRobot deployment architecture

Once the compute class is chosen, the deployment shape matters more than people expect. A LeRobot stack that runs on a laptop in a research office and a LeRobot stack that survives an external assessment are not the same artifact, even though the Python imports look identical.

The reference architecture Petronella Technology Group uses for regulated prototypes layers four planes. The robot data plane carries sensor streams from the Reachy Mini cameras, microphones, and inertial measurement unit, plus actuator setpoints from the policy. The training data plane carries dataset files, demonstration recordings, and replay buffers between storage and GPU nodes. The model artifact plane carries weights, checkpoints, and exported policies between training and inference hosts. The control plane carries operator commands, audit logs, and observability traffic. Each plane has its own segmentation, monitoring, and retention policy.

Storage is the unglamorous part that breaks first if it is undersized. A few hundred episodes of teleoperation data at 30 frames per second across one wide-angle camera and four microphones produces tens of gigabytes per hour of capture. Replay buffers for sim-to-real can easily exceed a terabyte. A regulated lab needs a tiered storage layout with hot solid-state storage for the active training run, warm spinning storage for completed datasets, and offline cold storage for archival snapshots. All three tiers need to live inside the assessment boundary and inherit its access controls.

Network design is where the LeRobot reference docs are most useful. The LeRobot cameras documentation describes the supported camera transports, including OpenCV-backed USB and built-in cameras, ZMQ-backed network cameras, Intel RealSense depth cameras, and Reachy 2 native cameras. Each transport has a different latency profile and a different threat surface. ZMQ over an unsegmented network is convenient for quick prototypes and dangerous for regulated work. The default we ship is camera traffic on a dedicated VLAN with mutual transport-layer security and no ingress from the broader corporate network.

Inference latency matters more than people think on a desktop humanoid. A behaviour cloning policy that responds in fifty milliseconds on a workstation feels alive. The same policy through a remote GPU at three hundred milliseconds round trip feels broken. The SmolVLA blog post from the LeRobot team is a useful primer on the synchronous-versus-asynchronous inference trade-offs, and it is one of the rare ML blog posts that takes "consumer-grade GPUs or CPUs" as a real deployment target rather than an afterthought.

Sim-to-real pipeline on private infrastructure

Sim-to-real is the practice of training policies inside a simulator and transferring them to a real robot. It is the only way to get enough data for many AI-driven robotics tasks, and it is also where private AI infrastructure earns its keep, because simulators run faster than real time, parallelize across GPUs, and produce datasets that the simulator owner gets to keep.

The Reachy Mini ships with a MuJoCo-based simulation software development kit per the Hugging Face launch announcement. MuJoCo is the canonical fast physics simulator for manipulation research and runs comfortably on the same GPU nodes used for training. Larger projects may layer NVIDIA Isaac, RoboCasa, or Genesis on top, depending on the scenario. The Hugging Face LeRobot ecosystem includes hooks for several common simulators and a growing collection of evaluation benchmarks.

The pipeline shape Petronella Technology Group recommends has five stages. First, capture a small set of high-quality teleoperation demonstrations on the real robot to seed the dataset. Second, train an initial policy on the demonstrations using a behaviour cloning algorithm such as the action-chunking transformer or the SmolVLA approach. Third, transfer the policy into simulation and run domain-randomized rollouts to collect a much larger dataset of policy executions across varied lighting, friction, and object placement. Fourth, retrain the policy on the augmented dataset. Fifth, evaluate on the real robot under controlled conditions and add the new evaluation episodes to the dataset for the next iteration. The loop closes faster on dedicated GPU infrastructure than on shared cloud, because the simulation rollouts can saturate the available compute without queueing.

The replay buffer is the artifact most likely to leak between iterations and the one most likely to attract a regulator's attention. It typically contains the raw observations, the actions taken, and any human feedback signals. For HIPAA-regulated work, those observations may include patient images. For ITAR-controlled work, they may include export-restricted geometry. The buffer must therefore be encrypted at rest, indexed in a way that allows authorized retrieval and deletion, and tied to an audit trail that proves who looked at what. Off-the-shelf object stores with proper access policies are the right tool. The wrong tool is a shared network drive nobody owns.

Evaluation cycles need their own discipline. Regulated robotics projects benefit from a cadence where every milestone closes with a security review, a results review, and a deletion review. The deletion review is the one labs forget. It asks "is there any data we no longer need that we should remove from the buffer," and it is the cheapest way to keep the dataset under the size where governance becomes painful.

Teleoperation security on regulated networks

Teleoperation, where a human operator drives the robot while the system records the motion, is the most common way to seed a behaviour cloning dataset. It is also the surface most often left wide open, because it is convenient and because Robot Operating System defaults were written before the current threat model existed.

The relevant protocol is Data Distribution Service, the publish-subscribe middleware that ROS 2 uses for inter-process and inter-host communication. ROS 2 supports security through the Secure ROS extension, which wraps DDS with authentication, access control, and encryption. The default implementation, however, is not enabled out of the box. A research lab that pulls down a fresh ROS 2 install and starts teleoperating against a robot on the same subnet is, by default, broadcasting the entire robot state in plaintext to anyone with network access.

The Petronella Technology Group default is to wrap the teleoperation session in a four-layer security model. Layer one is network segmentation, which puts the operator workstation, the robot, and the recording server on a dedicated isolated VLAN with no general internet egress. Layer two is mutual transport-layer security on every DDS topic, which requires an issued certificate on each participant and prevents rogue subscribers from joining the session. Layer three is signed and time-bound operator credentials, which expire automatically and are revoked the moment the session ends. Layer four is full session recording, which captures both the robot state and the operator inputs into an encrypted store under the same retention policy as the dataset itself.

Key management is the part most teams handle badly. Storing certificates and private keys in a shared filesystem is the path of least resistance and the path most likely to get a project into trouble. The default we ship pairs every robot and every operator workstation with hardware-backed key storage, rotates keys on a schedule that matches the project's risk profile, and integrates the rotation with the broader cybersecurity posture so that an audit can trace any key back to a person, a system, and a time window.

The same principles apply to the operator interface itself. Browser-based teleoperation is convenient and increasingly common, especially for the Reachy Mini's expressive head and antenna controls. Browser interfaces should run on the same isolated network, authenticate against the project's identity provider, and never expose a debug endpoint without authentication. The CISA Software Bill of Materials guidance is the right anchor for documenting the dependencies the operator interface pulls in, especially for projects that will be evaluated against NIST SP 800-218 SSDF practices.

Compliance overlay for defense, healthcare, and academic research

The compliance overlay depends on the funding source and the data type. The four most common patterns we see are CMMC for defense, HIPAA for healthcare research, ITAR for export-controlled designs, and the Common Rule plus Institutional Review Board oversight for human-subjects research. They overlap more than they diverge, which is good news for the architecture, because a well-designed sovereign stack can satisfy several of them simultaneously.

For CMMC Level 2 work, the controlling document is NIST SP 800-171 r3, which lists one hundred ten security requirements organized into fourteen families. The robotics-specific implications are mostly in the access control, system and information integrity, and audit and accountability families, and the practical work is documenting the boundary, the connections, and the monitoring. Petronella Technology Group is a CMMC Registered Provider Organization, RPO #1449, verifiable on the Cyber AB member directory, which means we can advise on assessment readiness even though the formal assessment itself is performed by an authorized C3PAO.

For HIPAA-regulated research, the additional anchor is the Security Rule and the Breach Notification Rule. Robotics datasets that include video or audio of patients fall under protected health information unless they are properly de-identified, and the de-identification process for video is non-trivial. The default we ship treats every clinical-research dataset as protected health information until proven otherwise and applies the same encryption, audit, and access controls regardless. The Common Rule adds Institutional Review Board obligations for human-subjects research and shapes how the team can recruit operators and subjects.

For ITAR-controlled work, the perimeter is territorial as well as digital. Export-controlled robot designs cannot be read by foreign persons regardless of where the file lives. The data plane, the operator pool, and the support staff all need to be inside the export boundary. The architecture implications are mostly about access management and personnel screening rather than the GPU hardware itself.

For Software as a Medical Device, the answer is "we do not do this work." The Food and Drug Administration's SaMD guidance imposes a separate regulatory regime, and the right partner for that scope is a firm with a quality management system aligned to ISO 13485. Petronella Technology Group focuses on prototyping, R&D, and demo builds rather than regulated medical device development.

Pricing model framing for custom robotics R&D

Robotics prototyping engagements are custom-quote work, and the reason is structural. Every project has a different data plane, a different compute footprint, a different compliance overlay, and a different success metric. Listing a single dollar figure on a webpage would either be wrong for most projects or wrong for the firm doing the work.

The shapes that do generalize are the engagement phases and the rate model. Petronella Technology Group offers four phase shapes for regulated robotics work. Discovery is a short fixed-fee engagement that produces an architecture document, a compliance map, and a scoped proposal for the next phase. Prototype is a fixed-scope engagement against a defined success metric, typically billed against milestones with a small contingency for environmental surprises. Harden is a fixed-scope engagement that converts a working prototype into something that survives an external assessment, including documentation, audit logging, and runbook authoring. Handoff is a knowledge-transfer engagement that trains the client team to operate the resulting stack.

For ongoing work, time-and-materials with a monthly cap is the common pattern. The cap matters because it gives the client predictability without forcing the firm to pad the estimate against unknown research questions. For very early-stage work where the goal is genuine exploration rather than delivery against a fixed metric, time-and-materials without a cap can also be appropriate, and we are direct about that conversation early.

For the prospective client trying to understand whether a conversation is worth their time, the right next step is the discovery phone call. That call is free, takes thirty minutes, and produces enough scope clarity for both sides to decide whether a paid engagement makes sense. Call (919) 348-4912 or use our contact form to schedule one.

Petronella's lens: our actual prototyping environment

This section is verifiable, so we use the depth. The lab the rest of this guide refers to is at our headquarters at 5540 Centerview Dr., Suite 200, Raleigh, North Carolina 27606. The Reachy Mini sits on a desk under a video-monitored ceiling next to a laser-printed signed ownership plaque, because the team takes a quiet pride in being the sort of firm that owns its own demo hardware rather than borrowing photos from a vendor press kit.

The compute footprint behind that desk is mixed by design. We run a small RTX PRO workstation in the lab itself for low-latency inference and quick behaviour cloning experiments. The bulk of training runs happen on an HGX class node in a separate machine room with three-phase power, segregated network, and badge access. Larger experiments and any consortium-scale work get scheduled against DGX-class compute either in our facility or in an aligned partner facility through the NVIDIA Elite Partner Channel.

The network architecture mirrors what we recommend to clients. The Reachy Mini lives on its own teleoperation VLAN with no internet egress. The training plane has its own segmentation, with explicit allowed flows to the Hugging Face Hub for model and dataset downloads only, and even those are gated through a logging proxy so we can answer the question "what model did the lab pull down on a given day" if a regulator asks. The control plane is on a third VLAN with mutual transport-layer security and hardware-backed certificates for every participant.

The operator pool today is small and named. Craig Petronella runs the discovery and security-architecture work personally. The cybersecurity team handles the network segmentation, monitoring, and incident response. The artificial intelligence team handles the training pipeline, the LeRobot integration, and the policy evaluation. Every operator has a CMMC Registered Practitioner credential, and the team is open about which engineer is doing which piece of the work.

Honest novelty disclosure: Petronella Technology Group's robotics practice is new in 2026. We do not have a portfolio of completed regulated robotics R&D engagements yet, and we will not pretend otherwise. We have a working lab, a sovereign GPU stack, twenty-three years of cybersecurity and compliance work under us, a Reachy Mini we own and run, and a clear positioning of what we do and what we do not do. That is what is on offer for the first wave of clients. The case studies will follow.

How to evaluate whether private AI robotics fits your project

The decision framework below is reproduced as a structured HowTo schema in the page header, so it should appear in search engines that surface step-by-step content. The same six steps work as a desk-side checklist for a principal investigator, a research engineering lead, or a defense program manager.

1. Classify the data. List every sensor stream, dataset, and weight artifact the project will produce. Tag each as Public, Controlled Unclassified Information, IRB-protected, ITAR-controlled, Protected Health Information, or Trade Secret. If any tag is non-Public, a sovereign stack is the safer default.
2. Map the regulatory perimeter. Enumerate the contracts, grants, and regulations that bind the project. DFARS 252.204-7012, NIST SP 800-171 r3, NIST SP 800-218 SSDF, HIPAA Security Rule, ITAR, EAR, NSF data management plans, and the Common Rule are the usual anchors. Each anchor implies specific boundary controls.
3. Inventory existing compute and storage. Audit GPU, CPU, network, and storage already inside the client data plane. Decide what gaps exist relative to the training and inference workload. NVIDIA DGX, HGX, or RTX PRO nodes sourced through the NVIDIA Elite Partner Channel close most gaps without breaking sovereignty.
4. Pick the robot platform. Match platform to research question. The Reachy Mini at $299 to $449 fits desktop expressive AI, perception, and teleoperation studies. SO-101, Koch v1.1, and the Unitree G1 cover other niches in the LeRobot supported-hardware list. Avoid forcing one platform onto every problem.
5. Design the trust boundary. Draw the data plane on paper. Decide where Hugging Face Hub assets enter, where ROS 2 DDS traffic flows, where teleoperation operators sit, and where weights live at rest. Apply NIST SP 800-171 r3 boundary controls and SBOM tracking per CISA guidance.
6. Plan the evaluation cycle. Define the success metric, the simulation environment, and the real-world replay set before training starts. Write the runbook for repeating the eval cycle without leaking data outside the boundary. Schedule a security review at the end of each milestone.

Common pitfalls and honest caveats

Sovereign and on-premises are not the same word, even though vendors have spent the last three years pretending they are. A sovereign cloud is still a cloud, owned and operated by a third party, with whatever boundary contractual constructions the vendor has built. An on-premises deployment lives inside the client data plane and inherits the client's existing controls. Both have legitimate uses. They are not interchangeable for the regulated robotics use case, and treating them as if they were is the most common mistake we see.

The second most common mistake is buying compute you do not need. A research group that asks for a DGX system because the literature uses one and ends up with a quarter-million-dollar machine running at five percent utilization on a behaviour cloning task is a recurring pattern. The right starting point is usually an HGX node, sometimes an RTX PRO workstation, with a clear plan for what would force a step up.

The third pattern is supply-chain risk in non-vetted compute. Refurbished GPUs from grey-market sellers, motherboards from compromised assemblers, and firmware images of unclear provenance all matter for regulated work. The NVIDIA Elite Partner Channel exists exactly to give buyers a chain of custody from manufacturer to dock, and the same logic applies to network gear, storage arrays, and the rest of the bill of materials. SBOM discipline starts with the hardware, not just the software.

The fourth pattern is the "we will get to compliance later" plan. Robotics R&D projects almost never have time to retrofit compliance after the fact, because the dataset has already accumulated and the architecture has already calcified. Building the boundary from day one costs less than rebuilding it the day before the assessment.

The fifth pattern, less obvious than the others, is treating the model and dataset artifacts as a project deliverable rather than a long-lived asset. A trained policy without a versioned training set, a written evaluation harness, and a reproducible build of the inference container becomes a black box the next post-doc cannot maintain. We push every regulated build to produce a model card with the data inputs documented, an evaluation report against a fixed test set, and a containerized inference image with a pinned software-bill-of-materials. The reason is mundane: the auditor and the next graduate student both ask the same questions, six months apart, and a project that cannot answer them has effectively shipped a one-shot prototype rather than a research artifact.

Frequently asked questions

What is private AI robotics prototyping?

Private AI robotics prototyping is the practice of building, training, and evaluating robot policies on a self-hosted GPU stack so that sensor data, manipulation traces, and learned weights never leave the client data plane. It pairs an open-source robot platform like the Reachy Mini with on-premises training infrastructure and the Hugging Face LeRobot ecosystem.

Why can't we use a public cloud LLM for regulated robotics R&D?

Public LLM endpoints retain prompts, route data through unspecified geographies, and cannot satisfy DFARS 252.204-7012 flow-down, NIST SP 800-171 r3 boundary controls, ITAR territorial rules, or HIPAA business associate constraints out of the box. For Controlled Unclassified Information, IRB-protected subject data, or export-controlled designs, a sovereign stack is the only path that survives an audit.

Do you really run a Reachy Mini in your lab?

Yes. Petronella Technology Group operates a Reachy Mini, the open-source desktop humanoid from Pollen Robotics that Hugging Face acquired in April 2025, in our Raleigh, North Carolina lab. We use it for behaviour cloning experiments, teleoperation studies, and sim-to-real evaluation on top of the Hugging Face LeRobot stack.

What GPU hardware do you use for training?

Petronella Technology Group sources NVIDIA DGX, HGX, and RTX PRO compute through the NVIDIA Elite Partner Channel and operates the gear inside our private network. Training scale is matched to the project: small behaviour cloning jobs run on a single RTX PRO node, while multi-task vision-language-action experiments run on multi-GPU HGX nodes.

Is this a managed service or a project engagement?

Both shapes are available. We run discovery to prototype to harden to handoff engagements as fixed phases or time-and-materials, and we offer ongoing managed runtime for the resulting stack. Pricing is custom-quote because the scope of regulated robotics R&D varies widely. Call (919) 348-4912 for a discovery conversation.

Do you do surgical robotics or production-line cobots?

No. Petronella Technology Group does not build FDA-regulated Software as a Medical Device, surgical robotics, or production-line industrial cobot integration. We focus on prototyping, R&D, demo builds, LeRobot integration, and on-premises inference for regulated research.

What compliance frameworks does the prototype stack align with?

Our default prototype baseline aligns with NIST SP 800-171 r3 controls for Controlled Unclassified Information, NIST SP 800-218 SSDF for secure software development, DFARS 252.204-7012 flow-down for defense work, HIPAA Security Rule for healthcare research, and IRB Common Rule data-handling for human-subjects work. Specific ITAR enclave architecture is available when export-controlled designs are in scope.

How honest are you about being new to robotics?

Petronella Technology Group has 23 years of cybersecurity, compliance, and private AI experience, and our robotics practice is new in 2026. We are open about that. The cyber, compliance, and GPU infrastructure under the robotics work is operational and verifiable today, and the Reachy Mini in our lab is owner-operated, not a stock photo.

Where to go next

If this guide matches a project you are scoping, the practical next moves are short. Read the parent Private AI Solutions pillar for the broader sovereign-AI story, the Robotics overview for the practice and the engagement shapes we offer, the Reachy Mini hardware page for the specific platform spec sheet, the Robotics Prototyping solutions page for the deliverables matrix, and the AI workstations page for the GPU options behind the stack. Then call (919) 348-4912 to schedule a discovery conversation, or use the contact form to send a written scope.

Petronella Robotics / Lead

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