CRF for Robotics: Epistemic Mismatch as a Control Signal for Perception, Adaptation, and Human Interaction

Robots deployed in open-world environments frequently fail not due to mechanical limitations or sensor noise, but due to an epistemic error: they continue to interpret and act as if their internal world model remains valid after the environment has changed. Traditional approaches address this problem through uncertainty estimation, novelty detection, or improved perception fidelity. This paper argues that these methods are insufficient because they do not address a more fundamental question — whether a robot is epistemically permitted to continue interpreting, learning, or acting under changed conditions. We propose an extension of the Cognitive Reflexivity Function (CRF) into robotics as a first-class control signal that detects model–world divergence and governs the legitimacy of perception interpretation and belief update. CRF reframes adaptation not as a question of confidence, but of epistemic permission: whether continued cognition remains valid given the mismatch between internal expectations and external evidence. We further introduce the Minimum Acceptable Evidence Doctrine (MAED) as a complementary governance layer that determines when action-enabling outputs — including physical motion and communicative intent — are permitted under epistemic uncertainty. Together, CRF and MAED form a layered epistemic governance architecture that separates the ability to compute from the right to act. The framework applies to sim-to-real transfer, continual adaptation in novel environments, and human–robot interaction where semantic misunderstanding can be as hazardous as physical error. This work positions legitimacy gating as a transferable design pattern for safer, faster-adapting robots operating under distribution shift.