Private Team Training

Humanoid and Physical-AI Security Training

Build the shared method your team needs to reason across firmware, ROS 2, cloud, perception and model-driven behavior, actuation, physical consequence, detection, stop, and recovery.
private team delivery • evidence-led • adapted to your target and release

Capability before a named release or deployment

A team needs more than isolated firmware, cloud, or model-security techniques when software authority can become physical action. It needs one way to model the whole control path and state what evidence would support a deployment claim.

The training is organized around that transformation: from an opaque cross-layer system to explicit invariants, prioritized scenarios, inspectable evidence, and a reusable retest plan.

Who it is for

  • Product security and application security teams responsible for robotics or autonomous systems
  • Robotics, platform, firmware, cloud, and safety engineers who need a shared security model
  • Security researchers and red teams moving from component testing to cross-layer control paths
  • Deployment and assurance leaders who must review evidence before a named release or operating environment

Eight connected modules

1. Physical-AI system and threat modeling

Map actors, robot and cloud components, trust boundaries, operating states, authority, safety constraints, and deployment claims.

2. Firmware, hardware, and update trust

Reason about boot, storage, credentials, debug interfaces, firmware provenance, update authorization, rollback, and recovery.

3. ROS 2 and DDS Security

Inspect nodes, topics, services, discovery, identities, policy, namespaces, bridges, and command authorization.

4. Cloud, fleet, and teleoperation

Analyze operators, roles, sessions, APIs, tenancy, support access, remote commands, connectivity loss, and auditability.

5. Perception, VLA, and autonomy boundaries

Separate sensor, model, retrieval, policy, tool, and planner outputs from the gates that authorize physical control.

6. Actuation and physical consequence

Trace commands to actuators, define physical impact, and distinguish reachability, acceptance, execution, and consequence evidence.

7. Detection, stop, and recovery

Design evidence for telemetry, alerting, independent stop, degraded mode, rollback, incident reconstruction, and restoration.

8. Deployment assurance and retest

Turn the model into invariants, scenarios, explorations, evidence packets, findings, remediation, and a release-specific retest plan.

Applied ROS 2 and physical-AI lab

A representative ROS 2 and simulation target is used to map nodes, topics, services, identities, authority, commands, state, and safe-stop boundaries. The team converts the model into invariants and controlled explorations.

Simulation establishes mechanism and workflow. It is not presented as independent physical-consequence proof or as a new vulnerability claim about a commercial robot.

Team artifacts, not just slides

  • A system and authority map for the selected training target
  • A small library of explicit, testable security invariants
  • Prioritized scenarios spanning software authority and physical consequence
  • Exploration records that preserve baseline, variation, observed delta, and evidence level
  • A deployment evidence packet and checklist your team can reuse
  • Detection, stop, recovery, remediation, and retest requirements

Delivery and scope

Delivery can be adapted around

  • The team's current architecture and deployment decision
  • Remote or on-site working sessions
  • A representative public/simulation target or an authorized internal target
  • Role-specific depth for security, robotics, firmware, cloud, and assurance teams

Useful inputs

  • Architecture, data-flow, and command-flow material
  • ROS 2 graph, policy, and deployed configuration
  • Operator roles, cloud/fleet flows, and safe-state requirements
  • A release, subsystem, or deployment question the team needs to answer

Evidence boundaries stay visible

  • No claim of a current commercial-humanoid vulnerability without validated evidence and responsible disclosure.
  • No claim that simulation proves physical reachability or independent physical consequence.
  • No client outcomes, logos, fixed dates, capacity, pricing, certification, or safety approval are implied by this page.
  • Platform examples are representative teaching targets, not endorsements.

FAQ

No. This is currently a private team training offer so the target, labs, constraints, and evidence artifacts can be aligned to the team's systems and deployment questions.
No. The baseline can use source, architecture, ROS 2, and simulation material. Any hardware-specific or physical-consequence work is separately scoped around access, safety, and authorization.
No. ROS 2 and DDS are important middleware examples, but the method transfers across firmware, cloud, teleoperation, autonomy, actuation, detection, stop, and recovery boundaries.
No. Representative public or simulation targets are used to teach system modeling and evidence discipline. A training exercise is not a claim of a new product vulnerability or vendor endorsement.
Yes. Training builds a shared method and artifact set; a separately scoped Physical-AI Deployment Assurance engagement can apply it to a named system, release, or deployment decision.
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