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

Researchers propose SPINE, a unified privacy-aware framework that treats privacy as a systemic architectural constraint throughout the entire Embodied AI lifecycle rather than isolated stage-level features. The position paper argues that current EAI systems optimizing individual components independently create cumulative privacy vulnerabilities in real-world deployments where data leakage is often irreversible.

The emergence of Embodied AI systems transitioning from controlled simulations into domestic and sensitive real-world environments presents a critical inflection point for privacy architecture in AI development. This research identifies a fundamental flaw in contemporary EAI design: treating privacy as modular patches applied to individual pipeline stages rather than foundational constraints shaping system behavior across boundaries. The implications are substantial. Current fragmented approaches to privacy—securing perception here, protecting planning there—fail to account for information leakage propagating through coupled stages, creating cumulative exposure in high-frequency deployments where irreversible data collection is characteristic of embodied systems operating in homes and sensitive locations.

Historically, AI safety and privacy discussions have lagged behind capability advancement, with privacy often retrofitted post-deployment rather than architected upstream. This position paper reflects growing recognition that EAI represents a qualitative shift requiring redesigned governance models. The SPINE framework's contribution lies in establishing privacy as a dynamic control signal that propagates constraints downstream, forcing developers to consider system-level privacy implications during design rather than after deployment.

For AI developers and institutions deploying EAI systems, this research signals potential regulatory and liability exposure. Privacy-by-design frameworks may become table-stakes compliance requirements as real-world EAI deployments proliferate. The preliminary validation through simulation and case studies establishes conceptual feasibility, but practical implementation challenges remain substantial. Developers must now consider whether existing EAI architectures adequately address lifecycle-level privacy, particularly as domestic robotics and smart home integration accelerate. The research community's direction toward unified privacy frameworks suggests future EAI competitive advantages will depend on privacy architecture depth alongside capability metrics.

• →Privacy in Embodied AI requires lifecycle-level architectural design rather than stage-local patches to prevent cumulative irreversible data leakage
• →SPINE framework treats privacy as a dynamic control signal governing cross-stage coupling throughout the entire EAI pipeline
• →Current fragmented privacy approaches in isolated EAI components create systemic vulnerabilities when deployed in sensitive real-world environments
• →Privacy-by-design principles are becoming essential for EAI systems operating in domestic and sensitive settings
• →Developers face emerging regulatory and liability risks if EAI systems lack unified privacy frameworks before deployment