Science for Robot Policy: advancing robotics policy through EU's science for policy approach
This paper proposes Science for Robot Policy, a structured and evidence-driven model that bridges the gap between service robotics innovation and regulatory adaptation. Using a Constructive Research Approach, the model integrates scientific experimentation, stakeholder engagement, and knowledge brokering to transform empirical data into actionable regulatory insights. The five-step process begins with risk identification and prioritization, proceeds through controlled testing environments, and aims to provide policymakers with tools to proactively address emerging safety and fundamental rights risks.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, policymakers, and standard-makers seeking to understand how to apply evidence-based science-for-policy approaches to robotics governance.
What the document contains
The paper addresses the regulatory disconnect in service robotics, analyses the shortcomings of existing safety standards such as ISO 13482, and outlines a five-step constructive research model incorporating risk identification, experimentation, data processing, knowledge brokering, and iterative policy refinement.
The text does not provide binding statutory text, an official legislative proposal, or enforceable regulatory mandates, focusing instead on a theoretical and methodological framework.
- Rapid advances in service robotics have outpaced traditional legal and standard-setting frameworks, creating regulatory disconnects and inconsistencies.
- The paper proposes a structured 'Science for Robot Policy' model utilizing a Constructive Research Approach to bridge innovation and regulatory adaptation.
- The model follows a five-step process starting with framework identification and risk prioritization, moving through controlled experimentation in simulators, testing zones, living labs, and real-world markets.
- Data gathered from testing and compliance tools must be systematically transformed into policy-relevant information and actionable knowledge through knowledge brokers.
- Empirical validation through EU projects like LIAISON and PROPELLING demonstrates that stakeholder engagement and real-world testing successfully surface regulatory gaps such as user diversity and safety oversights.
1. Introduction 2. Background information 3. Methodology and design mechanisms 4. Theoretical problem identification and understanding of the topic: Service robot regulatory disconnect 5. Designing a new construct: Science for Robot policy 6. Demonstrating that the new construct works: Stakeholder engagement and empirical validation 7. Showing the theoretical connections and the research contribution 8. Examine the scope of applicability of the solution 9. Conclusions
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
standards also face accountability issues given their usually non-binding nature and the limited mechanisms to oversee the validity of the content they define
1. Introduction
screening score · 2026-10-25
Human Dignity
1/5Respect for the inherent worth of persons
Cognitive and emotional aspects are often disregarded when these systems engage in social interaction with users and elicit emotional responses
2.2
screening score · 2026-10-25
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-25
Enforcement
4/5Consequences and compliance mechanisms with real force
Once robots are fully deployed in the market, observation of their operation tests regulatory and technology readiness, as real-world exposure reveals long-term issues
5.3
screening score · 2026-10-25
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
The model follows a five-step process, beginning with risk identification and prioritization, followed by controlled experimentation in simulators, testing zones, living labs, and real-world markets.
Abstract
screening score · 2026-10-25