Open Standard CC BY 4.0 · March 2026

MARS

Machine Assets Reporting Standard

The open neutral telemetry schema for autonomous machine performance, ROI calculation, and insurance underwriting. Any robot. Any manufacturer. One standard.

Download v0.9 Schema View AM-ROI Formula Contact
v0.9 Public Draft — Community Review Open
19 Schema Objects Defined
CC BY 4.0 — Free to Use and Adapt
v1.0 Target — Q2 2026
19
Schema Objects
1
Canonical ROI Formula
4
Integration Pathways
0
Manufacturer Dependencies

// The Problem

Every robot reports performance
differently — or not at all.

A BMW procurement team cannot compare Figure 02 against Atlas on the same economic metrics. An insurer pricing autonomous machine risk has no auditable data record to underwrite against. A CFO approving a 10-robot fleet has no neutral ROI calculation. MARS is the missing standard.

No Cross-Manufacturer Benchmark

Figure 02, Atlas, Digit, and AEON each report performance in proprietary formats. Direct comparison requires a neutral schema none of them currently produce.

Insurance Underwriting Is Blind

Insurers pricing autonomous machine risk work from manufacturer spec sheets, not live operational data. No auditable performance record exists for underwriting at scale.

ROI Is Uncomputable

Without standardized Task Success Ratio, Autonomy Score, and TCO inputs across manufacturers, genuine return on investment cannot be calculated or compared.

Resale Value Has No Basis

The secondary market for autonomous machines cannot function without performance provenance. A robot with 1,250 documented operating hours is worth more than one without — but currently there is no record.

One formula. Every robot.

The Autonomous Machine ROI (AM-ROI) is the canonical MARS performance metric — the economic return generated by a robot relative to its full cost of ownership, normalised against human labour as the baseline comparison.

AM-ROI = [ ( R + HLE × DF ) × TSR × AS ] / TCOannual

// Where TCO = (P/L) + C_energy + C_maint + C_operator + C_insurance + C_integration
RDirect revenue attributable to robot in period
HLEHuman Labour Equivalent — fully loaded annual cost
DFDisplacement Factor — degree of labour displacement (0.0–1.0)
TSRTask Success Ratio — tasks_completed / tasks_attempted
ASAutonomy Score — 1 minus human intervention rate
TCOTotal Cost of Ownership — all annual cost components

AM-ROI < 1.0 = below break-even  ·  AM-ROI ≥ 1.4 = production threshold  ·  AM-ROI ≥ 2.0 = strong return.
Full worked examples for Toyota TMMC (Agility Digit) and BMW Spartanburg (Figure 02 vs Atlas) in the methodology document.

Company names and deployment references are used for illustrative purposes only. Figures are estimates based on publicly available information. MARS is not affiliated with or endorsed by any manufacturer, operator, or insurer referenced herein.

// Schema v0.9

19 structured objects.
One auditable record.

MARS defines a JSON Schema (draft-07) standard any robot can report against — regardless of manufacturer, integration path, or deployment environment. Every record carries a SHA-256 audit hash chain.

machine_identity
Manufacturer, model, serial, firmware version, deployment environment
operational_metrics
Uptime, downtime, incident count, MTBI, task counts, autonomy score
financial_metrics
Purchase price, TCO components, AM-ROI, HLE, displacement factor, payback
ai_performance
Vision accuracy, hallucination rate, model version, drift index (JS divergence)
stability_safety
Falls detected, collision events, safety score, ISO compliance indicators
mars_risk_score
MRS composite score, risk class, premium tier — direct actuarial input
raas_sla
Parametric SLA triggers — TSR, uptime, AS thresholds with breach durations
depreciation
3-phase model: rapid initial → stable operational → terminal
audit_chain
SHA-256 hash per record, timestamp, validator signature

Built for the full stack
of the autonomous economy.

Insurance Underwriters

MARS Risk Score (MRS) is a direct actuarial input. Parametric SLA triggers automate claims. No more pricing AI risk from spec sheets.

Robot Fleet Operators

Compare performance across manufacturers on identical metrics. Calculate genuine AM-ROI. Substantiate resale value with performance provenance.

RaaS Providers

Standardized SLA compliance reporting. Parametric triggers for automatic credit and refund. Auditable performance record for every contract.

OEM Engineers

Benchmark your robot against deployment-type standards. Identify performance gaps. Demonstrate improvement trajectory to enterprise customers.

Financial Analysts

First standardized method for modelling autonomous machine capex, depreciation, and ROI. Consistent framework across any fleet or manufacturer.

Standards Reviewers

MARS v0.9 is a public draft actively seeking technical review. We explicitly invite challenge on benchmarks, formula weights, and methodology.

Everything is open.

All MARS documents are published under Creative Commons Attribution 4.0. Free to use, adapt, and redistribute with attribution.

// Contact

Insurer, operator, or engineer?
Let's talk.

We want your critique
more than your approval.

MARS v0.9 is a public draft. The benchmark values, formula weights, and methodology are explicitly open to challenge. If you are an actuary, robotics engineer, fleet operator, or standards professional and you see an error — we want to hear from you. That is how v1.0 gets built correctly.

Standard Version v0.9.0 — Public Draft
License Creative Commons BY 4.0
v1.0 Target Q2 2026 — With live pilot data

Company names, manufacturers, and deployment references on this site are used for illustrative purposes only. All performance figures and financial estimates are based on publicly available information and are not verified by or affiliated with the referenced companies. MARS is an independent open standard. It is not affiliated with, sponsored by, or endorsed by BMW, Toyota, Figure AI, Agility Robotics, Boston Dynamics, Munich Re, or any other entity referenced herein. MARS v0.9 is a public draft. Nothing on this site constitutes financial, legal, or insurance advice.