Humanoid Robot Deployment Provenance: Documentation for Integration Teams

A humanoid robot may have dozens of individually actuated joints, each with its own controller, each running its own firmware. It typically has multiple vision systems - RGB cameras, depth sensors, and sometimes event cameras - with different manufacturers and different firmware profiles. It has an audio system with its own processing stack.

It has a complex compute platform that may integrate multiple processors for different functional domains: a main control processor, a perception accelerator, a safety-dedicated processor, and potentially a cloud connectivity module. Each of these subsystems is a candidate for a provenance record: its manufacturer, its country of origin, and its firmware version all contribute to the complete provenance picture. For a humanoid deployment, the provenance documentation effort is more extensive than for simpler robot types, and the deployment team should plan for this effort in advance rather than attempting to complete it under time pressure at commissioning.

Prioritizing the subsystems with the highest security sensitivity and the most frequent regulatory scrutiny - compute hardware, AI accelerators, and communication modules - for the most detailed documentation is a practical way to manage the documentation scope.

Humanoid robots typically rely heavily on AI components for their perception, interaction, and planning capabilities. The provenance of AI components is more complex than the provenance of traditional hardware components because AI capability is determined not only by the hardware that executes it but by the models, training data, and software frameworks that constitute the AI system itself. For each AI component in a humanoid robot deployment, the provenance record should capture: the AI framework or platform on which the model runs and its version; the model architecture and version, if published by the manufacturer; the training data provenance, including the organization responsible for training, if the manufacturer makes this information available; and the update mechanism through which the AI model is updated after deployment, including who controls the update and what triggers it.

This level of AI provenance documentation is not always achievable from publicly available information, and the manufacturer's willingness to provide it varies. The provenance record should document what information was obtained, from what source, and what remains unknown, applying the same documentation discipline to AI components as to hardware components.

A configuration baseline for a humanoid deployment is more extensive than for a simpler robot because of the number and complexity of the configurable components. The baseline should cover: the firmware version for every joint controller; the software version for every major AI module; the calibration state for every sensor system; the active behavioral parameters for motion planning, safety, and interaction; and the integration configuration for any facility systems the robot connects to.

Capturing this baseline requires either comprehensive access to the robot's configuration reporting tools or a combination of tool-based query and physical inspection. Where the robot manufacturer provides a configuration reporting tool that exports a comprehensive configuration state, that export is the most reliable baseline capture method. Where no such tool exists, the baseline must be assembled from multiple sources and the record should note which fields were obtained from which source, with an assessment of the reliability of each source.

Humanoid robots often have ongoing relationships with the robot manufacturer that extend beyond the initial commissioning period. The manufacturer may provide over-the-air behavioral updates that improve the robot's performance or add new capabilities. The manufacturer's support team may need remote access to investigate issues or tune behaviors.

Third-party AI model providers may have update channels that deliver new model versions. Each of these relationships involves a remote access or data transmission capability that must be documented in the provenance record. For humanoid deployments in sensitive facilities, the access record is particularly important because the robot's sensing capabilities - cameras, microphones, and spatial awareness - make the access relationships more significant from a facility security perspective than they would be for a robot with less environmental sensing.

The access record for a humanoid deployment should be more detailed than for simpler systems, capturing not only who has access but what data those access channels can expose to the accessing party.

Humanoid robots have attracted procurement restriction attention from several governments and facility operators in recent years, reflecting concerns about the combination of general physical capability, environmental sensing, AI connectivity, and the manufacturing origins of some leading humanoid systems. For deployment teams placing humanoid robots in facilities with active procurement policies, the provenance documentation standard for humanoid deployments should meet the most detailed level described in this guide: complete subsystem records, AI component provenance, network and access documentation, and a clear record of the organizational identities behind every relationship the robot maintains.

A provenance record built to this standard is positioned to respond to a wide range of procurement review questions without requiring additional investigation. Dagmont records provenance and acceptance decisions; it does not certify compliance with procurement restrictions or regulatory requirements.

• Document the manufacturer, model, and country of origin for every major subsystem including each joint controller
• Record the firmware version for every firmware-carrying component in the as-delivered state
• Document the AI framework, model version, and update mechanism for every AI component
• Map all remote access relationships including manufacturer support and AI model update channels
• Capture a comprehensive configuration baseline using the manufacturer's configuration reporting tools where available
• Document all data transmission relationships including telemetry, model update, and remote support channels
• Review the completed provenance record against applicable procurement restrictions before acceptance