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Evaluate a physical AI platform as a complete engineering and operations system, not as a model demo. The right choice must connect to your existing ROS 2 graph, robot controllers, sensors and safety systems; produce repeatable evidence in simulation and on real hardware; run deterministically on the target edge computer; and provide a defensible path for safety, cybersecurity, updates and fleet support.
Use a representative pilot with one production-relevant task. Score interoperability, simulation and validation, model lifecycle, edge operations, safety, industrial fit and total cost of ownership against written acceptance criteria. A platform that wins a perception benchmark but cannot reproduce a policy, recover safely or roll back a faulty update is not production-ready.
What a physical AI platform includes
More than an AI model
In industrial robotics, “physical AI platform” describes the connected stack that turns sensor data into controlled physical action. It normally includes robot and sensor drivers, middleware, perception and planning software, model-training tools, simulation, edge runtime, fleet management, diagnostics and safety mechanisms. Some vendors package most of these layers; others provide an extensible foundation that you assemble with existing tools.
Your evaluation therefore covers the interfaces between layers as well as the quality of each layer. A detector that is accurate in isolation can still fail when camera timestamps drift, a controller rejects a command mode, a network loses packets or an update changes the robot description. Treat every hand-off as an engineering requirement.
#1 Best Overall
- The NVIDIA Jetson AGX Orin 64GB Developer Kit makes it easy to get started with Jetson Orin. Compact size, lots of connectors, and up to 275 TOPS of AI performance make this developer kit perfect for prototyping advanced AI-powered robots and other autonomous machines.
- The developer kit includes a Jetson AGX Orin 64GB module, and can emulate all the Jetson Orin modules. It supports multiple concurrent AI application pipelines with the NVIDIA Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed IO and fast memory bandwidth. Now you can develop solutions using your largest and most complex AI models to solve problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs the NVIDIA AI software stack, and use-case specific application frameworks are available, including Isaac for robotics, DeepStream for vision AI, and Riva for conversational AI. You can save significant time with NVIDIA Omniverse Replicator for synthetic data generation (SDG), and by using NVIDIA TAO toolkit to fine-tune pretrained AI models from the NGC catalog.
- Jetson ecosystem partners offer additional AI and system software, developer tools, and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for your product.
- With the computing capability of more than 8 Jetson AGX Xavier systems in a developer kit that integrates the latest NVIDIA GPU technology with the world’s most advanced deep learning software stack, you’ll have the flexibility to create tomorrow’s AI solution as well as today’s.
Why a lifecycle view matters
A production cell changes over time. Tooling wears, lighting shifts, products vary, software is patched and models are retrained. The platform must preserve traceability from a deployed policy to its training data, software build, robot configuration and calibration. It also needs a controlled way to test, approve, deploy and reverse changes. This lifecycle evidence is as important as the first successful run.
Set the acceptance task before comparing vendors
Start with one task that is representative of the intended deployment rather than a generic demonstration. The task definition becomes the contract against which every platform is tested.
Specify the operating envelope
- Workcell and robot: robot model, payload, reach, end effector, controller mode and permitted joint or Cartesian speeds.
- Sensors: camera types and placement, lidar or force sensors, frame rates, resolutions, calibration method and timestamp source.
- Environment: lighting range, reflective or transparent materials, clutter, occlusion, temperature, dust and expected human presence.
- Task outcome: the exact success condition, allowable positional or quality error and cycle-time target.
- Failure handling: which failures are acceptable, which require a controlled stop, how an operator is alerted and how the robot recovers.
- Production constraints: uptime target, maintenance window, network availability, data-retention policy and maximum energy or thermal limits.
Record the current automation or manual process as a baseline. Without a baseline, a platform can appear successful simply because the comparison point was never quantified.
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For each requirement, state the measurement method, test conditions, data set, owner and pass/fail rule. Keep safety-related requirements separate from AI-quality metrics. A high detection rate does not compensate for an unsafe stop path, and a fast policy does not prove that it behaves safely around people.
Use a seven-dimension scorecard
Score each candidate with the same evidence rules. A simple five-level scale works: 0 means no support or evidence, 1 means a roadmap or unverified claim, 2 means a limited demonstration, 3 means documented support in a pilot, 4 means repeatable production evidence and 5 means independently reviewed evidence with a maintained support commitment. Weight the dimensions according to the consequence of failure in your plant.
| Dimension | What to verify | Evidence to request | Typical risk if weak |
|---|---|---|---|
| Interoperability and portability | ROS 2 support, middleware and quality-of-service behavior, hardware drivers, controller interfaces, data formats, policy export and movement between workstation, cloud and edge | Working connection to your robot, sensors, network and safety PLC; source or schema for exchanged data; measured integration effort | Rework, proprietary adapters and inability to change hardware or deployment location |
| Simulation and validation | Physics configuration, robot-description import, sensor models, synthetic-data controls, reproducible scenarios, software-in-the-loop (SIL), hardware-in-the-loop (HIL) and a documented sim-to-real process | Scenario catalogue, seeded runs, coverage report, calibration procedure and gap analysis between simulation and the cell | Policies that work in a clean simulation but fail under real tolerances or disturbances |
| Task and model lifecycle | Data capture and labeling, synthetic-data generation, training or fine-tuning, evaluation sets, versioning, rollback and drift monitoring | Lineage linking a deployed model to data, code, configuration and approval record; rollback demonstration | Unrepeatable behavior and uncontrolled degradation after changes |
| Edge performance and operations | Sensor-to-action latency, throughput, determinism, power, thermal headroom, updates, diagnostics and fleet observability on target hardware | p50 and p95 latency, missed-frame counts, thermal logs, energy per cycle, update and recovery runbooks | Jitter, overheating, nuisance stops or an unserviceable fleet |
| Safety and cybersecurity | Hazard analysis, safe-state behavior, independent safety channels, security controls, incident logging, change control and certification plan | Safety architecture, threat model, test records, access controls, audit logs and mapping to applicable standards | Inability to demonstrate compliance or contain a dangerous fault |
| Industrial fit and ecosystem | Robot, PLC and fieldbus integrations, calibration, human-robot interaction, service coverage, spare parts and capable integrators | Reference architecture for your cell, named support responsibilities and a maintenance plan | Long commissioning delays and dependence on a single specialist |
| Economics and lock-in | Compute, sensors, licenses, cloud and simulation spend, integration and safety labor, support, retraining and switching cost | Five-year total-cost model, license terms, export rights and replacement scenarios | Low initial price followed by high operating or migration cost |
Test interoperability with the real ROS 2 and controller stack
ROS 2 is necessary but not sufficient
A platform may advertise ROS 2 support while still requiring proprietary nodes, message bridges or a specific middleware configuration. NVIDIA describes Isaac ROS as an open-source ROS 2 foundation intended to let developers retain familiar ROS tools and open standards while adding accelerated packages. That claim is useful only if the packages behave correctly in your graph and remain portable across your chosen hardware.
Test discovery, namespaces, parameters, lifecycle nodes, actions, services, QoS policies and clock synchronization. Exercise the network conditions expected in production, including bandwidth limits and temporary packet loss. Verify that recorded data can be replayed and that a node restart does not leave the robot in an uncontrolled state.
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Connect every boundary
- Connect the candidate to the actual robot controller in a non-production cell. Test the command modes, interpolation, watchdogs, limits and acknowledgement behavior that the controller exposes.
- Attach the production-intent sensors and validate driver timestamps, frame identifiers, calibration files, exposure controls and dropped-frame reporting.
- Connect the industrial network and safety PLC through the intended gateway or fieldbus. Confirm that ordinary software faults cannot bypass the independent stop circuit.
- Export a policy or model and run it on a second supported computer. Record which formats, accelerators, plugins or proprietary runtimes are required.
- Move the same workload from development workstation to cloud training or simulation and then to the target edge computer. Document every conversion, rebuild and manual step.
Count integration hours and data-conversion steps, not just whether a demonstration eventually works. Each undocumented adapter is a future maintenance liability.
Check portability explicitly
Ask what remains usable if you change robot brand, camera, GPU, operating system or cloud provider. A portable design keeps robot descriptions, sensor schemas, policy artifacts and evaluation data in documented formats. It also separates application logic from hardware-specific acceleration so that a replacement can be qualified without rewriting the task.
Demand a reproducible simulation-to-real validation path
Build a credible digital workcell
Import the robot description and tool geometry, configure joint limits and contact physics, and calibrate the virtual cell against measured poses. Add the same camera viewpoints and sensor characteristics used in production. Vary lighting, object pose, clutter, friction, tolerances and latency rather than testing only an ideal scene.
NVIDIA’s Isaac Sim learning material covers URDF import, physics, RGB cameras, 2D lidar and IMU sensors, domain randomization, SIL and HIL. Those capabilities are useful checkpoints when assessing any simulator: you should be able to represent the robot and sensors, deliberately vary conditions and run the policy through both software-only and hardware-connected loops.
Rank #2
- AGX Orin 64GB Development Kit makes it easy to get started with AGX Orin. Its compact size, rich interfaces, and AI performance of up to 275 TOPS make it ideal for building advanced AI robots and other autonomous machine prototypes.
- The development kit includes AGX Orin 64GB module and can emulate all Orin modules. It utilizes the Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed I/O, and fast memory bandwidth. You can leverage the largest and most complex AI models to develop solutions for problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs AI software and provides application frameworks for specific use cases, such as Isaac for robotics, DeepStream for visual AI, and Riva for conversational AI. Using Omniverse Replicator for Synthetic Data Generation (SDG) can save you significant time; while fine-tuning pre-trained AI models from the NGC catalog using the TAO toolkit can further enhance your results.
- Yahboom offers four kits for users to choose from. The AIlarge model voice module utilizes examples of AI large models and multimodal models; it provides 1TB/2TB SSDs with pre-flashed driver image files; and an 8MP USB industrial camera for image processing.
- It offers various online and offline mainstream AI large model development materials. The system is pre-configured with AI vision examples, ROS case studies, and AI large models. It supports offline/online deployment of large models for voice interaction, real-time video analysis, and visual positioning, helping you quickly get started with localized AI agent development.
Make runs repeatable
- Store the simulator version, robot and sensor models, environment parameters, random seed and policy hash for every run.
- Maintain a scenario catalogue that includes nominal, boundary and fault cases. Tag each scenario to a requirement or hazard.
- Use held-out evaluation scenes; do not tune a policy on the same scenes used to claim performance.
- Record the simulation-to-real gap in terms of observable differences such as detection confidence, pose error, cycle time, recovery frequency and stop events.
- Re-run a fixed regression suite after changes to the model, simulator, driver, robot calibration or edge runtime.
Progress through SIL, HIL and guarded hardware
SIL is appropriate for fast iteration and broad scenario coverage. HIL adds the real controller, timing and I/O behavior so that scheduling and interface faults appear before motion. A guarded physical pilot then tests effects that are difficult to model, such as cable movement, vibration, unmodeled friction, reflective surfaces and operator interaction.
There is no universal percentage of testing that can be done in simulation. Use risk and uncertainty to decide the balance. High-consequence motions, poorly characterized materials and interactions with people require more physical evidence. Simulation can provide breadth; hardware tests provide fidelity. Approve a task only when the combination covers its defined envelope and the remaining gap is understood and controlled.
Evaluate the data and model lifecycle
Trace every deployed policy
Require a lineage record that identifies the data collection session, labels, synthetic-data recipe, training code, hyperparameters, evaluation set, model or policy artifact, robot description, calibration, edge runtime and approval decision. If an operator reports a failure, the team should be able to reconstruct the exact configuration that produced it.
Test change management
- Capture a new data sample or synthetic scenario and label it with its source, conditions and quality checks.
- Train or fine-tune a candidate policy in an isolated environment. Preserve the previous production artifact.
- Run the fixed evaluation suite, including regression, boundary and safety-related cases. Compare results with the approved baseline.
- Deploy to a canary cell or limited fleet segment with enhanced logging and a defined observation period.
- Promote only after the owner signs the acceptance record. Keep a one-command or one-procedure rollback to the prior model, software and configuration.
Ask how the platform detects drift in sensor distributions, object mix, lighting, cycle time or recovery frequency. Monitoring should raise an actionable alert, not merely store an ever-growing log.
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Use end-to-end measurements
Benchmark the complete path from sensor exposure to commanded action on the computer that will be installed in the cell. Report median (p50) and tail (p95 or a stricter percentile selected for the task) latency, throughput, missed or late frames, scheduling jitter and behavior during concurrent workloads. A fast neural-network inference time is not an end-to-end guarantee.
Include startup time, memory pressure, storage wear, network interruptions and recovery after process or power restart. Measure thermal behavior over a production-length run, not only during a short demo. Log energy per cycle when power or battery capacity matters, and reserve thermal headroom for the warmest ambient condition in the operating envelope.
Assess deployment and observability
- Can software and model updates be staged, signed, paused and rolled back without an unsafe motion?
- Are health metrics available for sensors, clocks, GPU or accelerator load, temperatures, memory, queues and controller acknowledgements?
- Can operators distinguish a perception fault, communication fault, safety stop and planned maintenance stop?
- Does the fleet view show software, model, calibration and hardware versions per robot?
- Can logs be exported in a format your operations and incident teams can retain and analyze?
NVIDIA positions Jetson as an edge platform for deploying Isaac ROS workloads. If a Jetson developer kit is used during evaluation, treat it as a development and benchmarking computer unless the exact production module, carrier, thermal design and support lifecycle have been approved for the cell. Re-run all latency, determinism and thermal tests on the production-intent configuration.
Separate AI quality from safety and cybersecurity
Build an independent safety case
Start with a hazard analysis covering unexpected motion, incorrect perception, lost communication, stale data, actuator faults, power loss and human entry. Define the safe state for each hazard and show how the system reaches it when the AI process, operating system or network fails. The safety path should not depend solely on the model deciding to stop.
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Verify independent emergency-stop and protective-stop channels, speed and separation monitoring where applicable, controller limits, teach-mode behavior and restart authorization. Test fault injection: kill the perception process, disconnect a camera, corrupt a timestamp, saturate a queue and interrupt the network. Record the physical response and the time to a safe state.
Plan cybersecurity alongside functional safety
Use authenticated devices and updates, least-privilege service accounts, encrypted transport where required, signed artifacts, secure boot features available on the target hardware, network segmentation and tamper-evident logs. Define who can change a model, calibration or safety parameter and require review for each change. Include vulnerability response, patch windows and a method for isolating a robot while preserving safe operation.
Map evidence to standards and certification
NVIDIA describes its Halos program as spanning platform safety, operating-system safety and ecosystem or algorithmic layers, with a certification program covering functional safety, cybersecurity and physical-AI safety. Its June 22, 2026 announcement says the program draws on more than 18,600 engineering years of autonomous-vehicle safety development. Treat those statements as vendor evidence, not as a substitute for your own hazard analysis, integrator responsibilities or certification authority.
Rank #3
- 【Core Parameters】★AI Perf:34-67 TOPS ★GPU:512-core NVIDIA Ampere architecture GPU with 16 Tensor Cores ★CPU:6-core Arm Corte-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 ★Memory:4GB 64-bit LPDDR5 51 GB/s ★Storage: external NVMe via M.2 Key M (NOTE:SUB Board No SD Card Slot)
- 【Empowered by Large Al Model, Enhanced Human-Computer Interaction】Jetson Orin Super leverages three AI models and incorporates an AI voice interaction module. This multimodal visual system matches the scene being described, enabling environmental awareness and AI visual gameplay. Combined with a large-scale voice module and camera, it enables speech-to-text, semantic analysis, natural conversation, and real-time video analysis, enabling advanced embodied AI applications.
- 【AI Upgrade】Jetson Orin Nano series modules are compact in size but can deliver up to 34-67 TOPS of AI performance, with power consumption ranging from 7 watts to 25 watts. Compared to the Jetson Nano B01, it offers up to 80 times the performance and sets a new standard for entry-level edge AI.
- 【Highly compatible carrier board】Yahboom's carrier board is fully compatible with orin nano module. Compared to carrier boards that use Jetson Nano on the market, the newly upgraded circuit supports 25W power mode, which enables larger and more complex neural networks and fully leverages the performance of the core module. The resources, size, and interfaces of the Yahboom carrier board are consistent with the official board, with the only difference addition of power switch button.
- 【Tutorial materials provided】The JETSON system based on Ubuntu 22.04 provides a complete desktop Linux environment with accelerated graphics, supporting NVIDI-ACUDA 12.6, TensorRT 10.7.0, cuDNN 9.6.0, OpenCV 4.10.0, etc. The performance on AI LLM, VLM and visual Transformer is significantly improved compared with the previous generation.
The same announcement references IEC 61508, ISO 13849 and ISO/IEC TR 5469. Identify which standards apply to your machinery, region and risk classification, then agree with the integrator and certification body on the required work products. A platform can support the evidence process without itself making the complete machine compliant.
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Validate the parts that stop projects
Confirm support for the exact robot models, controller firmware, PLC family, fieldbus, safety controller, end effectors and calibration tools in your plant. Ask who owns troubleshooting when a fault crosses vendor boundaries. Review spare-parts lead times, regional service coverage, training, escalation and the integrator’s ability to maintain both conventional automation and AI software.
Human-robot interaction deserves its own test plan. Include operator access, handover, reduced-speed modes, unexpected entry, recovery after a protective stop and clear status indication. A technically compatible platform can still be a poor industrial fit if operators cannot understand or safely recover its behavior.
Interpret ecosystem claims carefully
NVIDIA has reported ecosystem work involving ABB Robotics, FANUC, KUKA, YASKAWA, Siemens and Universal Robots. It also reported that FANUC, ABB Robotics, YASKAWA and KUKA together have a global installed base exceeding 2 million robots in the context of those platform integrations. That figure describes the combined installed base cited by NVIDIA in 2026; it is not a guarantee that every model, controller or regional service organization is supported. Obtain a compatibility statement for your exact equipment.
Calculate total cost and lock-in
Build a five-year cost model
| Cost area | Questions to include |
|---|---|
| Compute and sensors | Edge computer or accelerator, carrier and thermal hardware, cameras, lidar, calibration fixtures, spares and replacement cycles |
| Software and infrastructure | Runtime or seat licenses, simulator capacity, cloud storage and training compute, observability and security tooling |
| Engineering | Integration, data collection and labeling, simulation, safety analysis, validation, commissioning and operator training |
| Operations | Support contract, monitoring, patching, incident response, calibration and periodic retraining |
| Change and exit | Cost to switch robot, sensor, accelerator, middleware, model format or vendor; retained skills and reusable data |
Separate one-time pilot costs from recurring per-robot or per-inference charges. Ask whether prices vary by deployment location, fleet size or cloud usage, and what happens when support for a hardware generation ends.
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Inspect export rights for robot descriptions, sensor data, labels, trained policies, evaluation results and logs. Determine whether an exported model can run with a documented open runtime or requires a proprietary compiler, key or hosted service. Record the effort to reproduce a build six months later and to deploy the previous version after a failed update.
Open interfaces reduce switching cost, but they do not eliminate it. Specialized accelerators, proprietary safety monitors, custom calibration and staff expertise can still create dependence. Put a migration exercise in the pilot: replace one noncritical component and document the work required to keep the task operating.
Run the evaluation in six controlled phases
- Define the acceptance task. Write the task, allowed failures, cycle-time target, recovery behavior, payload and environmental envelope. Capture the current automation baseline and obtain sign-off from operations, controls, safety and IT.
- Perform the interface test. Connect each candidate to the actual robot controller, sensors, network and safety PLC. Record integration hours, unsupported interfaces, data conversions, timing behavior and any proprietary dependencies.
- Recreate and test the workcell in simulation. Import the robot description, model sensors and vary lighting, pose, clutter and tolerances. Run the same policy in SIL with reproducible seeds. Require a scenario-coverage report and a written simulation-to-real gap analysis.
- Run HIL and a guarded physical pilot. Use the target edge computer and production-intent controllers. Measure p50 and p95 end-to-end latency, missed detections, collision or safety stops, recovery time, uptime, thermal behavior and energy per cycle. These are buyer measurements; do not accept unqualified vendor benchmarks in their place.
- Complete safety and operations review. Finish hazard and threat analyses, verify independent stop paths, inspect logs and update controls, and map evidence to the standards and certification bodies that apply to the machine.
- Make the scale decision. Approve expansion only when task thresholds are met, the support and spare-parts plan is funded, operators are trained, monitoring is live and rollback works for models, software and hardware configuration.
Compare finalists with a decision matrix
When two or more platforms pass the minimum gates, use a weighted matrix rather than allowing a single impressive demo to dominate. Set the weights before reviewing the final results. A safety-critical cell might give the largest weights to safety evidence and deterministic edge behavior; a research cell may weight portability and iteration speed more heavily.
| Comparison axis | Questions for the matrix | Evidence threshold |
|---|---|---|
| Open interfaces and portability | Can the stack use your ROS 2 graph, controllers and data formats, and can workloads move across approved computers? | Working pilot plus documented export and rebuild procedure |
| Robot, sensor, PLC and safety integrations | Are exact models and firmware supported, including calibration and independent stop paths? | Physical connection and fault-injection results |
| Simulation and SIL/HIL tooling | Can you reproduce scenarios, vary uncertainty and connect the real controller? | Seeded regression suite and gap report |
| Latency, determinism, power and thermal behavior | Does the target edge system meet tail-latency and environmental limits? | Long-duration logs from production-intent hardware |
| Data, model and fleet lifecycle | Can you trace, monitor, stage and roll back every artifact? | Demonstrated lineage, canary update and rollback |
| Safety, cybersecurity and certification evidence | Are hazards, threats, safe states and applicable standards addressed? | Reviewed safety case and agreed certification plan |
| Total cost and lock-in | What will the five-year cost and migration effort be? | Written commercial terms and replacement scenario |
| Ecosystem and service coverage | Can qualified people support the cell throughout its life? | Named contacts, response times, spares and training plan |
Vendor questions that expose hidden gaps
- Which ROS 2 distributions, middleware implementations and QoS profiles are tested, and how are security patches delivered?
- Which robot controllers and safety PLCs can be connected without a custom bridge? Who maintains that bridge?
- What exact model, policy and data formats can be exported, and can an exported artifact run offline?
- How are simulator versions, random seeds, robot descriptions and sensor models versioned?
- Show a SIL-to-HIL-to-physical workflow using the same policy and explain how discrepancies are reported.
- What are the measured p50 and p95 sensor-to-action latencies on the proposed edge computer under full workload?
- How does the system enter a safe state when the AI process, network, clock or accelerator fails?
- Which logs are retained, for how long, and can an incident be reconstructed without vendor access?
- How are model drift, calibration changes and unauthorized configuration edits detected?
- What is the rollback procedure, and how is it tested after a failed update?
- Which components are safety-certified, which require customer or integrator evidence, and which certification body will review the complete machine?
- What support response, spare-parts availability and software-maintenance period apply in the deployment region?
Red flags that should stop a purchase decision
- A demo uses a different robot, sensor arrangement or controller than the proposed cell, with no plan to reproduce the result.
- “ROS 2 compatible” means only that a one-way message bridge exists; timing, QoS, lifecycle and fault behavior are undocumented.
- Simulation results cannot be reproduced because versions, seeds or scenario parameters are missing.
- The vendor reports model accuracy but not end-to-end latency, tail behavior, thermal limits, recovery time or stop events.
- Safety is described as an AI confidence threshold rather than an independent, testable stop architecture.
- There is no immutable link between a deployed policy and its data, code, calibration and robot configuration.
- Updates cannot be staged or rolled back without taking the cell out of a safe state.
- Exported models, logs or labels require a proprietary service with no documented offline path.
- Support responsibilities between the platform vendor, robot maker and integrator are not written down.
- Commercial terms omit recurring simulation, cloud, support, retraining and end-of-life costs.
Make the final deployment decision
Choose the platform that produces the strongest evidence for your task, not the one with the broadest feature list. A passing decision requires all mandatory safety and interface gates, acceptable performance on production-intent hardware, a reproducible validation record, trained operators, funded support and a tested rollback path. If a candidate misses a noncritical threshold, document the compensating control and a dated remediation owner; do not hide the gap in an aggregate score.
For a multi-cell rollout, keep the first cell as a reference configuration. Freeze its robot description, calibration, software and policy artifacts, then expand through controlled variants. Re-run the relevant regression, safety and thermal tests whenever hardware, firmware, model, environment or deployment architecture changes. This discipline turns a one-off pilot into an auditable engineering capability.
The practical outcome of this process is not merely a vendor ranking. It is a set of acceptance tests, interfaces, safety evidence and operating procedures that remain useful even if the platform, robot or model changes. That portability is the clearest protection against technical and commercial lock-in.
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