Science for Robot Policy

Eduard Fosch-Villaronga, Mohammed Raiz Shaffique, Marie Schwed-Shenker, Antoni Mut-Piña, Simone van der Hof and Bart Custers

Technological Forecasting and Social Change, 2025, vol. 218, issue C

Abstract: The rapid advancement of service robotics has outpaced regulatory frameworks, leading to gaps and inconsistencies that hinder effective governance. While evidence-based policymaking is well-established in health and consumer protection fields, robotics regulation remains fragmented and reactive. This paper proposes Science for Robot Policy, a structured, evidence-driven model that bridges the disconnect between robotics innovation and regulatory adaptation. Using a Constructive Research Approach, the model integrates scientific experimentation, stakeholder engagement, and knowledge brokering to generate policy-relevant data and transform it into actionable regulatory insights. 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. The ambition is that insights generated are then translated into policy-relevant information and further refined into knowledge for policymakers, ensuring that empirical evidence informs that robotics regulation is dynamic, anticipatory, and informed. This approach contributes to ongoing discussions on science-for-policy methodologies and fosters iterative regulatory refinement in service robotics. If successful, such a model could allow policymakers to address emerging risks proactively, reduce regulatory uncertainty, enhance user safety, and promote responsible robotics innovation by embedding scientific insights into the policy cycle.

Keywords: Science for policy; Robotics regulation; Evidence-based policymaking; Service robotics; Knowledge brokering; ISO standard; Constructive research approach (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:218:y:2025:i:c:s0040162525002331

DOI: 10.1016/j.techfore.2025.124202

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