A sensor-equipped autonomous industrial cart operating in a warehouse under human safety supervision. Illustrative generated scene.

Helios for Physical AI & Embodied Systems

what’s next?

It works in simulation. What happens in the real world?

Connect simulation, physical constraints and feedback.
You define the outcome. Helios finds the path.

Physical AI & Embodied Systems

Connect simulation, physical constraints and feedback.

Real systems encounter noise, wear and changing conditions. Test where a policy transfers, where uncertainty grows and when a person or a safe fallback needs to take over.

Meet Helios Brain

Grounded in your reality

01Sensor and actuator limits

02Operating conditions

03Fallback and safety rules

Explore the Helios experience

Physical AI & Embodied Systems

DEMO

AI-generated, hypothetical plans. No live business data, verified professional advice or real actions.

Start with a decision

Do not enter sensitive information. Prompts are processed by AI and temporarily retained for this demo.

Choose a question or write your own goal0/5 steps connected

01
Define the outcome
02
Connect dependencies
03
Compare routes
04
Check constraints
05
Human approval

Choose a question or enter your goal.The AI will create five steps and three distinct routes.

ClearPressureBlockedAssumptionDependencyApproval gateEvidence

Helios across your ecosystem

Autonomy in the world. Accountability around it.

An embodied system encounters changing conditions that a simulation cannot fully capture. Human teams define where it may act, when it must defer and how it earns a wider operating envelope.

Decision authority

System owners, validation & safety authorities

Approve deployment policies, operating conditions and fallback requirements; review changes before increasing autonomy.

Helios supports

Compare simulated and observed behavior, expose uncertainty and evaluate safe operating limits.

A physical-AI test engineer reviewing observed conditions and safety boundaries on a tablet beside an autonomous cart. Illustrative generated scene. — Validation engineers & operational supervisors

Tablet at the test environment

Observe what the model missed.

Engineers assess real behavior against expectations. Their findings support an explicit review of deployment policy rather than an automatic decision to let the system do more.

Appropriate scope: Run approved evaluations, supervise operations and invoke safe fallbacks.
Review & escalation: System and safety authorities for unexpected behavior or changes to the operating envelope.
Evidence returned: Sensor limits, environmental changes, sim-to-real differences and fallback events.

Illustrative ecosystem and interfaces, not a live deployment. Roles, integrations and approval limits are defined with each organization.

From question to decision

See the options. Understand the consequences.

Explore the sim-to-real gap

Ground the comparison in your data, your objective and your constraints.

Evaluate fallback and deferral rules

Make dependencies and assumptions visible before you commit.

Feed observed outcomes into the next model

Carry the chosen path into a plan your team can review and act on.

Where to start

The decisions worth rehearsing.

Potential applications, scoped to your data, priorities and operating environment.

Sim-to-real transfer evaluation

Project the gap between simulated policy performance and real-world behaviour across distribution shift, sensor noise and actuator wear. Identify the deployments where transfer holds versus where it silently degrades.

Online-learning policy boundary

Test which adaptations the deployed policy is allowed to make in situ, and which require offline review. Calibrate the autonomy of learning to the consequence of error.

Calibrated abstention and fall-back control

Rehearse the conditions under which the model defers to a deterministic controller or to a human. Identify the abstention surface that holds safety without paralysing operations.

Hardware-software co-design trade-offs

Test alternative compute, sensor and actuator stacks against latency, power and thermal envelopes. Identify the architecture that lets the policy actually run.

Outcome attribution under hybrid control

When humans, deterministic controllers and learned policies share the loop, attribute outcomes correctly. For learning, for liability, for audit.

Embodied safety case

Construct an end-to-end safety case across hazards, failure modes, mitigations and evidence. Defend it against a regulator who knows physical systems but not learned policies.

What it takes to start

A useful pilot starts with the right foundations.

Agree a bounded decision, a baseline and acceptance criteria before introducing operational use. Scope and timing depend on your data, governance and intended application.

The data

Recorded sensor, actuator and environment observations
Simulation assumptions and real-world validation cases
Policy boundaries, exceptions and fallback outcomes

The people

System owner and safety sponsor
Validation, simulation and operations specialists
Hardware, perception and data-assurance owners

The evaluation

Sim-to-real sensitivity on held-out conditions
Uncertainty detection and correct fallback selection
Human escalation before policy-envelope changes

Before operational use

Bounded, supervised physical test environment
Validated safety mechanisms independent of the example
Named policy authority, logging and incident procedures

When Helios should not act

Knowing when to stop is part of the decision.

Missing evidence, uncertain conditions and absent authority require different responses. A recommendation must not silently become an action.

The signal

Sensor or environmental evidence is unreliable.

Appropriate response: Defer action according to the approved fallback and request inspection.
Responsible review: Operational supervisor and sensor-validation owner
Before the path resumes: Sensing and environmental conditions are within validated limits.

The boundaries matter

Your rules are part of the model.

Regulatory context shapes how a system should be designed and reviewed. Your legal, compliance and domain teams remain part of that process.

IEC 61508 / ISO 26262 functional safety

EU AI Act + EU Machinery Regulation combined obligations

Sector-specific clinical, automotive, aerospace and industrial standards

Provenance and ledger requirements for hybrid human-AI decisions

Context for implementation, not a certification or guarantee of regulatory compliance.

Trust and governance

Connected challenges

A different world. The same question.

Explore all 15 industries

Senior credit and risk leaders reviewing lending policy together in a bank meeting room. Illustrative generated scene.

01 / 15

Banking

Bring your decision · Physical AI & Embodied Systems

Start with the decision that matters to you.

The outcome, the boundaries and the people responsible. A focused starting point for a conversation about your organization.

Use high-level descriptions only. Do not include confidential, personal, patient, financial-account or classified information.