Towards Considerate Embodied AI: Co-Designing Situated Multi-Site Healthcare Robots from Abstract Concepts to High-Fidelity Prototypes

2026-02-03

AI Summary

Designing context-aware, needs-driven embodied AI systems that alleviate non-value-added burdens and enhance human-AI collaboration in high-stakes clinical settings remains a significant challenge. This study addresses this gap through a 14-week multidisciplinary co-design workshop involving 22 participants across three clinical contexts—emergency care, rehabilitation, and sleep clinics—employing iterative high-fidelity prototyping. The work uniquely integrates educational scaffolding, multi-context coverage, and a sustained co-design process to bridge abstract concepts with practical implementation. It proposes eight design principles for embodied AI, emphasizing contextual sensitivity, social responsiveness, and deployment feasibility. Findings demonstrate that translating conceptual ideas into high-fidelity prototypes through this structured approach effectively enhances both the practical utility and human-centered qualities of embodied AI systems in real-world healthcare environments.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Co-design is essential for grounding embodied artificial intelligence (AI) systems in real-world contexts, especially high-stakes domains such as healthcare. While prior work has explored multidisciplinary collaboration, iterative prototyping, and support for non-technical participants, few have interwoven these into a sustained co-design process. Such efforts often target one context and low-fidelity stages, limiting the generalizability of findings and obscuring how participants'ideas evolve. To address these limitations, we conducted a 14-week workshop with a multidisciplinary team of 22 participants, centered around how embodied AI can reduce non-value-added task burdens in three healthcare settings: emergency departments, long-term rehabilitation facilities, and sleep disorder clinics. We found that the iterative progression from abstract brainstorming to high-fidelity prototypes, supported by educational scaffolds, enabled participants to understand real-world trade-offs and generate more deployable solutions. We propose eight guidelines for co-designing more considerate embodied AI: attuned to context, responsive to social dynamics, mindful of expectations, and grounded in deployment. Project Page: https://byc-sophie.github.io/Towards-Considerate-Embodied-AI/

Problem

• embodied AI
• co-design
• healthcare robotics
• multi-site
• non-value-added tasks

Innovation

• co-design
• embodied AI
• high-fidelity prototyping
• healthcare robotics
• multidisciplinary collaboration