构建可信赖的具身AI需整合测试、验证与运行时保障
Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
- 通过场景化测试与覆盖率指标提升可靠性评估
- 利用符号化表示实现系统行为的分层验证
- 支持部署中适应不确定性的实时保障机制
具身AI系统正越来越多地部署于开放世界环境中，但其可靠性仍是根本性挑战。基于AAAI'26桥梁项目关于‘通过测试与形式化验证实现具身AI可靠性的讨论’，本文指出具身AI的可靠性本质上是生命周期保障问题，源于不确定性、人机交互以及紧密耦合系统组件中的涌现行为。我们提出三个互补方向：(1) 基于验证规范和有意义覆盖度量的可信场景化测试；(2) 通过系统行为与环境上下文的结构化符号表示实现组合式验证；(3) 具备应对部署中不确定性与分布偏移能力的运行时保障机制。我们主张将这些方法集成，通过共享神经符号表示和全生命周期持续反馈，形成贯通测试、验证与运行时适应的综合保障流程。该集成框架为在复杂真实环境中安全可靠运行的具身AI系统提供了基础。
原文摘要 · Abstract (English)
Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Bridge Program on "Making Embodied AI Reliable with Testing and Formal Verification", this article argues that reliability in embodied AI is inherently a lifecycle assurance problem arising from uncertainty, human interaction, and emergent behaviors across tightly coupled system components. We identify three complementary directions toward reliable embodied AI: (1) trustworthy scenario-based testing supported by validated specifications and meaningful coverage metrics, (2) compositional verification enabled by structured symbolic representations of system behavior and environmental context, and (3) runtime assurance mechanisms capable of adapting to uncertainty and distribution shifts during deployment. Rather than treating these approaches independently, we advocate integrated assurance workflows that connect testing, verification, and runtime adaptation through shared neuro-symbolic representations and continuous feedback across the system lifecycle. Such integration provides a foundation for building trustworthy embodied AI systems that can operate safely and reliably in complex real-world environments.
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