Eliciting Understandable Architectonic Gestures for Robotic Furniture through Co-Design Improvisation

2025-01-03

AI Summary

Human–robot communication in adaptive architecture—particularly with robotic furniture—faces challenges in conveying spatial intent unambiguously through nonverbal means. Traditional deictic or symbolic gestures prove insufficient for expressing architectural-scale actions. Method: This study introduces “architectural gestures”—a novel paradigm leveraging spatial pose and motion as communicative carriers. Through interdisciplinary, improvisational co-design workshops, we integrated motion capture, Laban movement analysis, gesture elicitation, and thematic coding to systematically uncover embodied interaction strategies. Contribution/Results: We identified 20 distinct gesture strategies and established a morphology-to-intent mapping framework covering six core architectural intentions (e.g., partitioning, expanding, retracting). User experiments demonstrate significant improvements in recognition accuracy and behavioral predictability of robotic spatial actions. The framework provides a scalable theoretical foundation and practical design methodology for nonverbal, human–robot cohabitation in adaptive built environments.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Interaction Techniques and Devices

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

The vision of adaptive architecture proposes that robotic technologies could enable interior spaces to physically transform in a bidirectional interaction with occupants. Yet, it is still unknown how this interaction could unfold in an understandable way. Inspired by HRI studies where robotic furniture gestured intents to occupants by deliberately positioning or moving in space, we hypothesise that adaptive architecture could also convey intents through gestures performed by a mobile robotic partition. To explore this design space, we invited 15 multidisciplinary experts to join co-design improvisation sessions, where they manually manoeuvred a deactivated robotic partition to design gestures conveying six architectural intents that varied in purpose and urgency. Using a gesture elicitation method alongside motion-tracking data, a Laban-based questionnaire, and thematic analysis, we identified 20 unique gestural strategies. Through categorisation, we introduced architectonic gestures as a novel strategy for robotic furniture to convey intent by indexically leveraging its spatial impact, complementing the established deictic and emblematic gestures. Our study thus represents an exploratory step toward making the autonomous gestures of adaptive architecture more legible. By understanding how robotic gestures are interpreted based not only on their motion but also on their spatial impact, we contribute to bridging HRI with Human-Building Interaction research.

Problem

• Adaptive Architecture
• Robot Furniture
• Gesture Communication

Innovation

• Adaptive Architecture
• Gestural Communication
• Human-Robot Interaction