Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels

2026-07-28

AI Summary

Current embodied intelligence systems prioritize task completion over sustained safety, struggling to provide trustworthy assurances in dynamic and uncertain environments. This work proposes a novel four-layer collaborative framework that integrates embodied AI, robotics, control theory, and trusted computing to establish a hierarchical trustworthiness architecture. For the first time, it unifies task capability, safety, system assurance, operational governance, and evidential support within a non-normative layered structure. By incorporating uncertainty calibration, fault isolation, runtime monitoring, and structured assurance arguments, the framework enables quantifiable evaluation under constrained deployment conditions. This study lays a theoretical and architectural foundation for the systematic design of trustworthy embodied intelligence, guiding research priorities and future standardization efforts.

Abstract

Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, traceability, and structured assurance arguments. The deployment layer maintains claim validity through runtime monitoring, authority management, intervention, incident response, and controlled updates. Because assumptions and failures propagate across these layers, neither model capability, isolated safeguards, nor benchmark performance alone can establish end-to-end trustworthiness. Drawing on embodied AI, robotics, control, dependable computing, distributed systems, and autonomous driving, we further propose a non-normative hierarchy of trustworthiness levels. This hierarchy grades the strength of bounded deployment claims across task capability, safety, system assurance, operational governance, and supporting evidence, providing a basis for bounded deployment, comparative evaluation, research prioritization, and future standardization.

Problem

• trustworthy embodied intelligence
• sustained safe success
• risk management
• system assurance
• deployment validity

Innovation

• Trustworthy Embodied Intelligence
• Sustained Safe Success
• Systems Framework
• Graded Trustworthiness Levels
• End-to-End Assurance