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.

翻译：具身人工智能（EAI）系统正迅速从模拟环境向现实世界的居家及其他敏感场景转型。然而，近期EAI解决方案的进展大多集中在指令理解、感知、规划与交互等孤立阶段，未考虑在隐私泄露往往不可逆的高频部署场景中这些阶段耦合带来的隐私影响。本立场论文认为，在敏感环境中独立优化各组件将引发系统性隐私危机，由此提出EAI中的隐私应被视作贯穿生命周期的架构性约束，而非阶段局部特征。针对上述挑战，我们提出“下一代具身AI中的安全隐私集成”（SPINE）框架——一种统一感知隐私的架构，将隐私视为贯穿EAI全生命周期、调控跨阶段耦合的动态控制信号。SPINE将EAI流水线分解为多个阶段，并构建基于多准则的隐私分类矩阵，以协调跨阶段边界的上下文敏感性。我们开展初步仿真与真实世界案例研究，从概念层面验证隐私约束如何向下游传播以重塑系统行为，揭示碎片化隐私补丁的不足，并驱动面向安全且功能性完整的具身AI系统的未来研究方向。SPINE框架及案例研究的详细信息见：https://github.com/rminshen03/EAI_Privacy_Position。