Towards Falsifiable Grammars of Human-Robot Interaction

Ensuring intuitive and trustworthy collaboration between humans and robots requires interactions that are predictively verifiable, not merely reactive. In many canonical HRI scenarios—such as object handovers, joint attention, or turn-taking—current systems detect coordination failures only after breakdowns occur and lack a formal basis for defining interaction success in advance. We introduce a minimal, falsifiable grammar of interaction that models coordination using a small set of primitives (signal, request, response, acknowledge) and temporal operators (sequence, concurrency, disruption, repair). By binding these operators to measurable budgets of latency, synchrony, and retry counts, the grammar specifies when interactions succeed, deviate, or require repair. This enables robots to anticipate and respond to interactional breakdowns (e.g., delayed reaches or missed acknowledgments) before task failure occurs. The framework provides a path toward auditable, predictable, and human-aligned robot behavior, grounding formal verification methods in everyday HRI practice.