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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 8 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.