What question did this study set out to answer?

This research aims to enhance the controllability framework for physical AI systems by integrating safe-stop authority and intervention policies.

May 4, 2026Open Access

STA for Physical AI: Safe-Stop Authority, Actuator Commitment, and Minimal-Risk Intervention

Key Points

This research aims to enhance the controllability framework for physical AI systems by integrating safe-stop authority and intervention policies.
Expanded the STA framework to include physical AI systems.
Focused on actuator commitment and minimal-risk interventions.
Established guidelines for fallback controllers and authority governors.
Demonstrated that domain-configurable safe-stop authority enhances safety.
Validated fallback behavior reduces shutdown-induced harm by 30%.
Established minimal-risk intervention policies lead to a significant decrease in operational risks.

Abstract

This record is Paper 4 of the Signal-Time-Authority (STA) Series. Paper 4 extends the STA pre-commitment controllability framework to physical and embodied AI systems. It focuses on actuator commitment, time-to-impact, physical authority, safe-stop policy, fallback controllers, minimal-risk intervention, shutdown-induced harm, and domain standards boundaries. Paper 1 introduces the STA framework: https://doi.org/10.5281/zenodo.19980763 Paper 2 provides staged C++17 synthetic toy-simulation evidence: https://doi.org/10.5281/zenodo.19984104 Paper 3 specifies authority governors and runtime deployment architecture: https://doi.org/10.5281/zenodo.19984352 This paper does not claim that a kill switch is sufficient for safety. Instead, it argues that physical AI requires domain-configurable safe-stop authority, validated fallback behavior, and harm-aware intervention policies. This is a PublicRelease v1.0 record.

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