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Risk Academy, led by the Nexus Consortium · Course catalogue · Technology and Digital Systems

Artificial Intelligence and Autonomous Systems

Robotics and autonomous systems

Robotics and autonomous systems, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Robotics and autonomous systems: foundations and practice

This area covers robots and autonomous systems that act in the physical world, from industrial and collaborative robots to autonomous vehicles, drones and uncrewed systems.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Creates a common approval basis for vehicles without a human fallback, shaping safety evidence expected from developers. United Nations Economic Commission for Europe, UNECE adopts first-ever global rules allowing fully autonomous vehicles, 24 June 2026

Learning outcomes

  1. Define collaborative robot and the other key terms of the discipline and use them correctly in a written case
  2. Explain industrial and collaborative robots and functional safety of machinery with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against autonomous vehicles, stating evidence, date and limits

Modules

  1. Industrial and collaborative robots
    • Industrial and collaborative robots
    • Functional safety of machinery
    • Autonomous vehicles
  2. Drones and uncrewed aircraft
    • Drones and uncrewed aircraft
    • Operational design domains
    • Human-machine teaming
  3. Cyber-physical security
    • Cyber-physical security
    • Liability and governance of autonomy
  4. Lab: the discipline's method
    • To show a failure rate below a target with stated confidence and no failures observed, a fleet must drive minus ln(1 minus confidence) divided by the target rate; for fatality-class events that is hundreds of millions of kilometres, so a deployment decision needs an argument built from more than fleet data.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-T11 Robotics and autonomous systems risk
  • NRS-H100 Hazard family: Robotics, autonomy and autonomous weapons
  • NRS-M19 Method fabric: Cyber, AI and frontier technology

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-C8 Evidence, uncertainty and provenance

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. United Nations Economic Commission for Europe, UNECE adopts first-ever global rules allowing fully autonomous vehicles, 24 June 2026
  2. Kalra and Paddock, Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?, 2016
  3. Koopman and Wagner, Autonomous Vehicle Safety: An Interdisciplinary Challenge, 2017
  4. Burton et al., Mind the gaps: Assuring the safety of autonomous systems from an engineering, ethical, and legal perspective, 2020
02Live map

Hazards and exposure in the Caribbean

Flood water, active fires and surface heat beside population and night-time lights, over the Caribbean.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards and exposure

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Hazards and exposure, framed on the Caribbean.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological SurveySources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Live: U.S. Geological Survey Events are indicative, not authority warnings.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (10 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

What is changing, with standards and evidence

Robotics and autonomous systems: frontier methods

The methods, data, tools and studies that changed robotics and autonomous systems between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Robotics and autonomous systems: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Creates a common approval basis for vehicles without a human fallback, shaping safety evidence expected from developers. United Nations Economic Commission for Europe, UNECE adopts first-ever global rules allowing fully autonomous vehicles, 24 June 2026

Learning outcomes

  1. Explain what changed in robotics and autonomous systems since 2024 and cite the studies, data or rules behind each change
  2. Reproduce UN adopts first global rules for driverless automated driving systems on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. UN adopts first global rules for driverless automated driving systems
    • The UNECE World Forum WP.29 adopted a global technical regulation and a UN Regulation on automated driving systems, built on safety cases, safety management systems, credible testing and in-service monitoring, and amended about ninety existing regulations.
    • Creates a common approval basis for vehicles without a human fallback, shaping safety evidence expected from developers.
    • United Nations Economic Commission for Europe, 24 June 2026
  2. FAA proposes routine beyond-visual-line-of-sight drone operations
    • The FAA proposed Part 108, a framework allowing routine beyond-visual-line-of-sight drone flights without individual waivers, covering aircraft up to 1,320 pounds and uses such as delivery, agriculture and surveying.
    • Would shift drone risk management from case-by-case waivers to standing operator permits, approvals and deconfliction duties.
    • Federal Register (Federal Aviation Administration), 7 August 2025
  3. A revised standard updates industrial robot safety requirements
    • ISO published the third edition of its industrial robot safety standard, covering inherently safe design, risk reduction and user information for robots as partly completed machinery, replacing the 2011 edition.
    • Is the reference for functional safety and risk assessment of industrial and collaborative robot installations.
    • International Organization for Standardization, 1 February 2025
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-T11 Robotics and autonomous systems risk
  • NRS-H100 Hazard family: Robotics, autonomy and autonomous weapons
  • NRS-M19 Method fabric: Cyber, AI and frontier technology

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-C8 Evidence, uncertainty and provenance

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. United Nations Economic Commission for Europe, UNECE adopts first-ever global rules allowing fully autonomous vehicles, 24 June 2026
  2. Federal Register (Federal Aviation Administration), Normalizing Unmanned Aircraft Systems Beyond Visual Line of Sight Operations, 7 August 2025
  3. International Organization for Standardization, ISO 10218-1:2025 Robotics – Safety requirements – Part 1: Industrial robots, 1 February 2025
  4. Kalra and Paddock, Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?, 2016
  5. Koopman and Wagner, Autonomous Vehicle Safety: An Interdisciplinary Challenge, 2017
  6. Burton et al., Mind the gaps: Assuring the safety of autonomous systems from an engineering, ethical, and legal perspective, 2020
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Collaborative robot

A robot designed to work alongside people in a shared space.

Term

Operational design domain

The conditions, such as roads, weather or speeds, under which an autonomous system is designed to work.

Term

Functional safety

Safety that depends on control systems working correctly in response to their inputs.

Term

Uncrewed aircraft system

An aircraft flown without a pilot on board, with its control station and links.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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