Robotic Governance: A Regulatory Framework for Autonomous Machines

Humanoid robots are walking through warehouses. Autonomous vehicles make evasive decisions in milliseconds. Surgical assistance systems perform procedures without a surgeon directly at the instrument. None of these are future scenarios - they describe the operational reality of 2024. The EU AI Act is in force, and covers AI in products and safety functions. The physically acting machine that moves, lifts, touches, protects and can injure people also requires machinery law, technical standards and operational responsibility. This broader need for a governance framework is the starting point for Robotic Governance - a concept anchored in academic research since 2016 and more urgent today than it has ever been.

What Robotic Governance Means

I developed the concept through my doctoral research at the Technical University of Munich, supervised by Klaus Mainzer. One foundational publication is Technology Governance, which Martina Bode and I published at IEEE EmergiTech in 2016. An extended chapter followed in a Springer volume on Robotic Governance. Both publications established the concept academically and defined its scope.

Robotic Governance refers to the regulatory and ethical framework for the responsible research, development, implementation, and management of increasingly intelligent and autonomously acting machines. The approach is holistic: it involves research, society, politics, industry, religious institutions, and trade unions, aiming for consensus through discourse ethics - not one-sided top-down regulation. Source: https://en.wikipedia.org/wiki/Robotic_governance

Robotic Governance is not a subdivision of Corporate Governance, IT Governance, or Technology Governance. Corporate Governance deals with organizational management, board accountability, and shareholder obligations. IT Governance addresses the strategic oversight of information systems. Technology Governance handles the broader societal embedding of technology. Robotic Governance is both more specific and broader: it explicitly addresses systems that act physically in the world, that exert force, that move through space, and that come into direct contact with human bodies. A chatbot that delivers incorrect information is a problem. An autonomous transport system that makes the wrong decision injures people. That distinction is what justifies a dedicated governance framework. I discuss my view of how these systems evolve in my retrospective on the four robotic revolutions. The full Wikipedia article on the concept is available at: en.wikipedia.org/wiki/Robotic_governance. It also describes the Robot Manifesto as a product of the discourse ethics approach - behavioral codes and procedural norms such as ethics commissions, designed to resolve conflicts case by case before political or regulatory intervention becomes necessary.

The Gap in the AI Act

The EU AI Act, which entered into force in 2024, is a serious first attempt to bring artificial intelligence under regulatory control. It classifies AI systems by risk level, mandates transparency requirements, and defines prohibited applications. That is necessary and correct. But the AI Act regulates AI as a software system. A language model, an image classifier, a recommendation algorithm - those are its typical objects. Robots are more. An industrial robot weighing 200 kilograms, moving components with 1,500 newtons of gripping force, is not a software system. It is a mechatronic system with actuators, sensors, real-time control loops, and physical effects in the world. The AI Act covers the embedded intelligence in that system - but not the machine itself. What the AI Act does not regulate:

Movement and physical interaction: How an autonomous system exerts force on people or objects, which safety distances apply, how emergency stops are implemented - these fall under machinery directives, ISO 10218, and sector-specific standards, not the AI Act.
Hardware supply chains: Who bears responsibility when a robot assembled from 47 components supplied by 23 vendors fails in its AI-guided function? The AI Act does not answer this.
Human-machine interaction in safety-critical environments: How a surgical assistance system communicates with a surgeon, how a collaborative robot responds to human proximity during a production shift - these require independent regulation.
Autonomous systems in public spaces: Delivery robots on sidewalks, humanoid care assistants in nursing homes, autonomous security robots in shopping centers - all operate in a regulatory grey zone.
Lifecycle responsibility and recall: What happens when an autonomous system shows different behavior after an update to its AI model? Who is liable? The EU's revised Product Liability Directive of 2024 was not primarily conceived for learning, evolving systems, and the interface remains unresolved.

The consequence is concrete: a company developing an autonomous industrial robot with integrated AI today must simultaneously comply with the AI Act, Machinery Directive 2006/42/EC, the new Machinery Regulation (EU) 2023/1230, relevant ISO standards, and sector-specific requirements - without anyone having assembled these pieces into a coherent framework. The compliance effort is real, the overlaps are complex, and the room for interpretation is wide. That is precisely the task of Robotic Governance: not to create yet another regulatory instrument, but to bring the existing building blocks into a coherent structure.

The Five Pillars of Robotic Governance

Robotic Governance is not a single instrument. It is a framework that brings together different levels of governance. Five pillars form the structure - developed and described in the original 2016 publications and extended in academic discourse since then. 1. Legal Framework Laws and regulations provide the binding foundation. The EU AI Act is one building block, not a complete structure. What is needed: clear liability rules for autonomous systems. When an AI-controlled robot arm injures a worker in production, the liability question is open in many jurisdictions today. Who bears responsibility - the robot manufacturer, the system integrator, the operating company, or the developer of the AI model? The EU's revised Product Liability Directive addresses some aspects, and expressly addresses software, AI and products that continue to learn after being placed on the market. National implementation and transition rules must still be considered. Additionally, the legal personhood of machines in specific contexts needs clarification: an autonomous vehicle acting as a contracting party, or a care robot making independent decisions about medication administration in a nursing home - clear legal categories for these situations are still missing. The EU Machinery Regulation (EU) 2023/1230 is a step forward, but it was developed without explicit coordination with the AI Act. That gap between the two instruments is the real structural problem, and it will only widen as systems become more capable. 2. Ethical Guardrails Legal minimum requirements are not sufficient when machines make morally charged decisions. Who has priority: the passenger in an autonomous vehicle or the pedestrian? How does a robot's self-protection function weigh against a nearby human? How does a humanoid care robot evaluate competing needs of a patient with dementia? These questions cannot be fully codified in standards. Ethical guardrails must therefore be integrated into development processes as explicit design requirements - not as a retrospective check, but as input to system architecture. The IEEE 7000 Standard for ethically aligned system design offers an applicable framework: it defines methods for identifying ethical requirements, weighting competing values, and documenting ethical design decisions in a form that survives an audit. 3. Technical Standards Standards translate abstract requirements into concrete engineering specifications. Without them, governance stays abstract. For Robotic Governance, the most relevant include:

ISO 10218-1: Safety requirements for industrial robots. Part 1 concerns the robot itself; Part 2 concerns integration and the safety of the complete robot application and cell. Protective zones and safety distances therefore require consideration of the application context. For collaborative robotics, ISO/TS 15066 extends this framework with specific requirements for human-robot collaboration.
VDA 5050: Interface standard for autonomous mobile systems in industry and logistics. It enables cross-manufacturer communication between vehicles and control systems - making it a technical governance measure that can support interoperability and reduce vendor dependence. A company implementing VDA 5050 creates the foundation for transparent, auditable system control.
IEEE 7000: Ethically aligned system design as a standard - a methodological basis for integrating ethical requirements into development processes, from requirements specification to acceptance testing.

Standards are not bureaucratic overhead. They are the language in which governance becomes operational. A company able to demonstrate conformity of its robot applications with the relevant ISO 10218 requirements and operating VDA 5050 can demonstrate to customers, regulators, and insurers what its system can do - and what it cannot. 4. Economic Incentives Regulation alone is not enough. When compliance costs more than the calculated risk of an incident, compliance is avoided - particularly in small and medium-sized enterprises where resources are constrained. Robotic Governance therefore needs incentive structures that make compliance economically attractive. Multiple mechanisms can achieve this: insurance models that reward demonstrably safer systems with lower premiums; certification pathways that facilitate market access rather than blocking it; public procurement rules that prefer governance-compliant systems. A company with reliable evidence that its robot applications meet the relevant ISO 10218 requirements, operating VDA 5050, and with IEEE 7000 integrated into its development process should have measurable advantages in public tenders. That would be governance with economic effect - not just a compliance exercise. 5. Societal Dialogue Technical systems operating in care facilities, schools, public spaces, and production environments require social legitimacy. This is not generated by press releases, but through structured dialogue. That means: citizen participation in municipal robotics projects before the first systems are deployed, not after; union involvement in the introduction of autonomous factory systems before the decision is made, not after; open, empirically grounded debate about robots in sensitive contexts such as childcare, elderly care, or corrections facilities. Robotic Governance without societal dialogue is technocracy. With structured dialogue, governance becomes a durable social contract - one that holds even when a system fails and the public debate begins. Because in that moment, what gets evaluated is not just the system, but the process through which it entered the world. I describe how businesses discuss these questions of responsibility in my EDAY 2026 retrospective.

Robotic Natives and Generation R

All five pillars address current actors: companies, regulators, engineers, ethicists, union representatives. But governance is not a snapshot. It must also be designed for the people who will grow up with these systems - and who in twenty years will make the decisions about what robots are permitted to do. That is the context in which the concept of Generation R was introduced - first at the Gartner CIO Summit in 2013, then in a peer-reviewed publication at IEEE EmergiTech 2016. Generation R, also called the "Robotics Generation," is the first generation growing up with robots as part of daily life: household robots, autonomous vehicles as standard transport, robotic toys as a first technological companion, and collaborative systems in the workplaces of their parents. Members of this generation are called Robotic Natives - by analogy with the Digital Natives of Generations Y and Z. The difference is substantial: Digital Natives grew up with screens and networks, with information systems that process data. Robotic Natives grow up with systems that act physically, that occupy space, that exert force, and that make decisions with physical consequences in the real world. The concept of the Robotic Native is documented in the academic literature originating from this research line. This different relationship with technology creates different expectations about how it should be governed. For Robotic Governance, this has concrete implications:

Education: Technological literacy in working with autonomous systems must be anchored in school curricula - not as an elective, but as foundational education. A person who does not understand how an autonomous system makes decisions, what data it uses, and what error types it has cannot evaluate or challenge those decisions. That is a democratic question, not only a technical one.
Work: Job profiles are changing. Not because robots "take all the jobs" - that claim is empirically thin and historically unreliable. But because new roles are emerging that require different qualifications: robot supervision during production shifts, system integration across heterogeneous fleets, governance compliance as a dedicated function, AI-assisted quality assurance. Education and training systems are not yet aligned with this reality.
Society: Generation R will evaluate governance questions differently from today's decision-makers. People who grew up with autonomous systems ask different questions: not primarily "is it safe?" - that baseline is assumed -, but "who is specifically responsible when it fails?" and "how can I as an affected person exercise influence?" That is a more mature and more demanding governance posture - and it must be built into the system before that generation reaches decision-making positions.

What Companies Should Do Now

Robotic Governance is not a topic for 2030. Anyone developing, procuring, or operating autonomous systems today is already acting in a regulatory environment that is consolidating rapidly. The Machinery Regulation (EU) 2023/1230 applies from 2027. The AI Act is being applied in phases, with rules on prohibited AI practices and AI literacy applying from 2025 and the main high-risk requirements following later. Five measures that are implementable now:

Conduct a governance inventory: Which autonomous systems are in operation or in procurement? Which fall under the AI Act, which under the Machinery Regulation, which under ISO 10218? Which fall into high-risk categories? These overlaps must be mapped before the first audit arrives - not after. A company that cannot answer these questions has a compliance gap, regardless of how good its systems technically are.
Make VDA 5050 a binding technical standard: For companies operating multiple AGV or AMR systems from different manufacturers, VDA 5050 is no longer optional - it is an operational necessity. Companies still running proprietary interfaces are building vendor lock-in today that they will spend significant effort unwinding in three years. They are also foregoing the transparency that auditable system control requires.
Set ISO 10218 as the safety baseline: ISO 10218-1 defines the safety standard for industrial robots. It should serve as a reference not only for new purchases but also for existing systems in the installed base. A gap analysis against ISO 10218 is the first step toward defensible safety documentation.
Define internal governance roles: Who in your organization carries responsibility for the governance of autonomous systems? If the answer is "legal will handle it" or "that's IT security's domain," that is a structural gap. Robotic Governance requires technical, legal, and ethical competence in one role - or in a structured, cross-functional body with clear decision authority.
Adopt IEEE 7000 as a development methodology: The IEEE 7000 Standard for ethically aligned system design is not a philosophical document - it is an applicable methodology. It defines how ethical requirements are formulated in specifications, implemented in system designs, and verified in acceptance testing. That is governance you can document and audit.

No company needs to implement all five points simultaneously. But every company operating autonomous systems should know exactly where it stands on each one - and be able to put that assessment in writing.

Conclusion

Robotic Governance is not an academic construct waiting for better conditions. It is a direct response to a concrete governance gap: autonomous machines act physically in the world, but the regulatory framework was built for software systems. That discrepancy grows with every new robot generation - with every new application domain, with every humanoid system entering a new societal context. The EU AI Act is an important step - but it is not sufficient. It also covers product-related AI and safety functions. Machinery law and technical standards likewise address control systems and safe applications. The practical task is to connect these requirements. Between these lies a space that needs structure. Robotic Governance describes and organizes that space. And it is not a brake on innovation - it is innovation's prerequisite: a company that cannot explain how its system decides, who is liable, and which standards apply will not be permitted to operate that system in safety-critical applications. Those who do not start understanding this framework and implementing it internally will be unprepared at the next regulatory cycle. Generation R will demand accountability, not statements of intent. Auditors will want documentation, not slide decks. And the first serious lawsuit against an autonomous system will ask whether the company had an internal governance process - or just a robot. The Robotic & AI Governance Foundation works to close these gaps - through concrete frameworks, academic publications, and the goal of establishing Robotic Governance as an independent field that brings together technology, law, and society.

Related reading added on 18 October 2026: My article AI After 60 shows how understandable explanations and participation connect in everyday life. A question at an Augsburg event for older adults becomes a case for trying technology, checking its results and having a say in society.