Privacy as Architectural Constraint in Embodied AI
A recent position paper asserts that privacy in Embodied AI (EAI) should be regarded as an architectural constraint applicable throughout the entire life cycle, rather than just a feature relevant to specific stages. The authors argue that independently optimizing elements such as instruction, perception, planning, and interaction leads to a systemic privacy crisis in sensitive real-world applications. They introduce SPINE (Secure Privacy Integration in Next-generation Embodied AI), a comprehensive framework that positions privacy as a dynamic control signal influencing cross-stage interactions across the full EAI life cycle. This paper has been made available on arXiv with the ID 2605.05017.
Key facts
- Embodied AI systems are moving from simulations to real-world domestic environments.
- Recent EAI solutions focus on isolated stages without considering privacy implications.
- Privacy leakage in high-frequency deployments is often irreversible.
- The paper argues for privacy as a life cycle-level architectural constraint.
- SPINE is proposed as a unified privacy-aware framework.
- SPINE treats privacy as a dynamic control signal across stages.
- The paper is a position paper on arXiv (2605.05017).
- The approach covers the entire EAI life cycle.
Entities
Institutions
- arXiv