Science for Robot Policy

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. • Robotic innovation outpaces laws, risking user rights and causing regulatory gaps. • Science for policy could align innovation with societal needs for robot regulation. • Science for robot policy iteratively tests solutions to refine robotics regulations. • Knowledge brokers can bridge developers, policymakers, and end-users effectively.