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This paper identifies three key dimensions of safety for foundation model (FM)-enabled robots: action, decision, and human-centered safety, arguing that existing methods are insufficient for open-ended real-world scenarios. To address this, they propose a modular safety guardrail architecture with monitoring and intervention layers to ensure comprehensive safety across the autonomy stack. The paper further suggests cross-layer co-design strategies, such as representation alignment and conservatism allocation, to improve the speed and effectiveness of safety enforcement.
Foundation model-powered robots need more than just physical constraints鈥攖hink modular safety guardrails that understand context, human intent, and evolving norms.
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.
