E-UHR: An Ethical Uncertainty Handling and Response Framework for Embodied AI Co-Habitats

Karthik Srikumar

Keywords: Embodied AI, Uncertainty Handling, Ethical Human-Robot Interaction

TL;DR: E-UHR is a theoretical framework enabling embodied AI to resolve uncertainties ethically through bi-directional, multimodal human-agent communication.

Abstract: Embodied AI agents, powered by Vision-Language Models (VLMs) and Vision-Language-Action models (VLAs), are increasingly deployed in shared human-agent co-habitats, where collaboration hinges on effective uncertainty handling. However, resolving uncertainties through bi-directional communication introduces technical and ethical challenges, such as privacy risks, cognitive overload, and safety-critical consequences, which remain underexplored. Existing approaches to uncertainty in robot learning and ethical human-robot interaction lack a unified framework tailored to the multimodal, real-time demands of embodied co-habitats. This paper introduces the Ethical Uncertainty Handling and Resolution (E-UHR) framework, a novel theoretical architecture that integrates uncertainty detection, ethical query formulation, human response integration, and verification with adaptation to foster trustworthy and robust interactions. We propose a taxonomy of uncertainties (perceptual, semantic, contextual, and ethical) and map their associated risks. E-UHR embeds ethical priors to mitigate model hallucinations and offers design principles for VLM/VLA integration alongside developer guidelines for equitable human-agent collaboration. By addressing the intersection of technical uncertainty and ethical imperatives, E-UHR lays a foundation for safe, transparent, and intuitive embodied AI systems, with broad applicability to future architectures. This work aims to guide developers and researchers toward creating co-habitats that prioritize human-centric values, setting the stage for empirical validation in simulated and real-world settings.

Submission Number: 6