HRI Grounding: Evidence That Operators Authorize and Correct AI Multi-Robot Plans Through a Generated Supervisory Interface

Abstract

AI planners can now generate a task plan for a team of robots, yet a human operator must stay in control of what the robots do. Doing so requires an interface that makes the plan visible and each step actionable, so mistakes can be caught before they reach the robots. Today, such an interface is developed for each specific application. This letter shows that non-expert operators can supervise an AI-generated multi-robot plan, authorizing sound plans and correcting faulty ones before any robot acts. The interface is generated automatically by HRI grounding, an architecture that renders any plan in a pre-defined format as one control per step, each showing in plain language what a robot will do to which target and holding the command it runs on the operator’s confirmation. Across 100 generated plans over two domains, five planner models, and two interface engines, HRI grounding produced a working interface and dispatchable ROS commands for nearly every plan, with no specific interface developed for any particular plan, domain, or planner. About one in five plans contained a semantic fault. In a study with sixteen operators, participants distinguished correct plans from faulty ones with high sensitivity and few false alarms ($d^{\prime }\!=\!2.90$), and through the interface recovered 85 of the 96 missions that would otherwise have failed unsupervised. Operating a mission through the HRI grounding interface also lowered completion time, interactions, errors, and workload relative to a manual baseline. Commands dispatched by HRI grounding also drive a physical Unitree Go2 under ROS with live plan regeneration.