Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World

2026-02-03

AI Summary

This work addresses the multidimensional safety challenges—spanning action, decision-making, and human-centered considerations—that foundation model–driven robots encounter in open, dynamic, and long-tailed real-world environments, which existing approaches struggle to handle effectively. The paper presents the first systematic three-dimensional framework for robotic safety and introduces a modular safety guardrail architecture comprising monitoring and intervention layers. This architecture enables cross-layer coordination through mechanisms such as representation alignment and conservatism allocation, thereby delivering full-stack, scalable, and composable safety guarantees. Crucially, the design supports dynamic adaptation to evolving tasks and environments, offering a flexible and efficient safety deployment paradigm for physical AI systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning and embodied physical action. These challenges require safety notions beyond physical constraint satisfaction. In this paper, we characterize FM-enabled robot safety along three dimensions: action safety (physical feasibility and constraint compliance), decision safety (semantic and contextual appropriateness), and human-centered safety (conformance to human intent, norms, and expectations). We argue that existing approaches, including static verification, monolithic controllers, and end-to-end learned policies, are insufficient in settings where tasks, environments, and human expectations are open-ended, long-tailed, and subject to adaptation over time. To address this gap, we propose modular safety guardrails, consisting of monitoring (evaluation) and intervention layers, as an architectural foundation for comprehensive safety across the autonomy stack. Beyond modularity, we highlight possible cross-layer co-design opportunities through representation alignment and conservatism allocation to enable faster, less conservative, and more effective safety enforcement. We call on the community to explore richer guardrail modules and principled co-design strategies to advance safe real-world physical AI deployment.

Problem

• foundation models
• robot safety
• modular safety
• human-centered safety
• real-world deployment

Innovation

• modular safety guardrails
• foundation models
• robot safety
• cross-layer co-design
• human-centered safety