Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces

TL;DR

This paper proposes a structured, multimodal framework for communicating human intent to robots in industrial settings, aiming to improve trust, safety, and efficiency in human-robot collaboration.

Contribution

It introduces a multidimensional design space for intent communication based on SAT framework and task abstraction levels, guiding multimodal interaction design.

Findings

Framework guides multimodal communication strategies

Highlights open questions and design challenges

Lays foundation for a future interaction design toolkit

Abstract

As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we propose a multidimensional design space that maps intent content (SAT1, SAT3), planning horizon (operational to strategic), and modality (visual, auditory, haptic). We illustrate how this space can guide the design of multimodal communication strategies tailored to dynamic collaborative work contexts. With this paper, we lay the conceptual foundation for a future design toolkit aimed at supporting transparent human-robot interaction in the workplace. We highlight key open questions and design challenges, and propose a shared agenda for multimodal, adaptive, and trustworthy robotic collaboration in hybrid work environments.