Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
For researchers and practitioners in embodied AI, this paper provides a conceptual framework and community agenda for addressing reliability challenges, though it remains at the proposal stage without concrete results.
The paper argues that reliability in embodied AI is a lifecycle assurance problem and proposes integrated workflows combining scenario-based testing, compositional verification, and runtime assurance through shared neuro-symbolic representations.
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.