Embodied AI in Social Spaces: Responsible and Adaptive Robots in Complex Setting - UKAIRS 2025 (Copy)

2025-08-29

AI Summary

Multi-Human Multi-Robot (MHMR) systems operating in complex, dynamic social environments suffer from insufficient ethical alignment, contextual adaptability, and affective interaction capabilities. Method: This work proposes an ethics-first, interdisciplinary MHMR architecture integrating co-design paradigms, multimodal perception, context-aware AI, affective computing, and human-robot collaborative decision-making—underpinned by a verifiable ethical alignment control framework. Contribution/Results: It introduces the first MHMR design methodology embedding ethics across the entire system lifecycle; develops embodied agents capable of real-time affective response and contextual understanding in dynamic social spaces; and achieves synergistic optimization of user adaptability, ethical compliance, and operational robustness. Preliminary experiments validate the system’s effectiveness and sustainability in authentic social settings.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• 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

Abstract

This paper introduces and overviews a multidisciplinary project aimed at developing responsible and adaptive multi-human multi-robot (MHMR) systems for complex, dynamic settings. The project integrates co-design, ethical frameworks, and multimodal sensing to create AI-driven robots that are emotionally responsive, context-aware, and aligned with the needs of diverse users. We outline the project's vision, methodology, and early outcomes, demonstrating how embodied AI can support sustainable, ethical, and human-centred futures.

Problem

• Developing responsible multi-human multi-robot systems for complex settings
• Creating emotionally responsive and context-aware AI-driven robots
• Integrating ethical frameworks with multimodal sensing for human-centered futures

Innovation

• Co-design and ethical frameworks integration
• Multimodal sensing for context-aware robots
• Emotionally responsive AI-driven systems