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

2025-06-18

AI Summary

To address trust deficits, safety risks, and efficiency bottlenecks arising from inadequate intent understanding in industrial human–robot collaborative assembly, this paper proposes a multimodal intent communication framework. Methodologically, it introduces a novel three-dimensional design space integrating Situation-Aware Transparency (SAT), task abstraction levels, and multimodal dimensions—enabling adaptive customization of intent expression under dynamic operational conditions. The framework unifies an SAT-driven transparency mechanism, hierarchical task modeling, multimodal (visual, auditory, haptic) interaction channels, and context-responsive strategies. Key contributions include: (1) the first design principles and prototype toolbox for transparent human–robot collaboration; (2) a systematic characterization of critical challenges and implementation pathways for multimodal, adaptive, and trustworthy coordination; and (3) empirically demonstrated improvements in human–robot mutual intelligibility, trust, and operational safety.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Ensuring mutual understanding in human-robot industrial collaboration
• Designing multimodal intent communication for dynamic work contexts
• Building transparent and trustworthy human-robot interaction frameworks

Innovation

• Multimodal framework for human-robot intent communication
• SAT-based design space mapping intent content
• Adaptive communication strategies for dynamic collaboration