Position: Embodied AI Requires a Privacy-Utility Trade-off

TL;DR

This paper emphasizes that privacy considerations in Embodied AI should be integrated across the entire system lifecycle, proposing a unified framework to manage privacy-utility trade-offs in real-world deployments.

Contribution

It introduces SPINE, a comprehensive privacy-aware framework that manages privacy as a dynamic control signal across all stages of Embodied AI systems.

Findings

Privacy constraints influence downstream system behavior.

Fragmented privacy solutions are insufficient for real-world deployment.

Preliminary case studies validate the importance of integrated privacy management.

Abstract

Embodied AI (EAI) systems are rapidly transitioning from simulations into real-world domestic and other sensitive environments. However, recent EAI solutions have largely demonstrated advancements within isolated stages such as instruction, perception, planning and interaction, without considering their coupled privacy implications in high-frequency deployments where privacy leakage is often irreversible. This position paper argues that optimizing these components independently creates a systemic privacy crisis when deployed in sensitive settings, thereby advancing the position that privacy in EAI is a life cycle-level architectural constraint rather than a stage-local feature. To address these challenges, we propose Secure Privacy Integration in Next-generation Embodied AI (SPINE), a unified privacy-aware framework that treats privacy as a dynamic control signal governing cross-stage coupling throughout the entire EAI life cycle. SPINE decomposes the EAI pipeline into various stages and establishes a multi-criterion privacy classification matrix to orchestrate contextual sensitivity across stage boundaries. We conduct preliminary simulation and real-world case studies to conceptually validate how privacy constraints propagate downstream to reshape system behavior, illustrating the insufficiency of fragmented privacy patches and motivating future research directions into secure yet functional embodied AI systems. We detail the SPINE framework and case studies at https://github.com/rminshen03/EAI_Privacy_Position.