<![CDATA[The Operational Design Domain (ODD) concept provides a structured framework for defining the operational boundaries within which an automated system is designed to function safely. Although ODD has become an established concept in automated driving and is increasingly reflected in regulatory requirements, no equivalent cross-platform taxonomy has been established for general-purpose Physical Artificial Intelligence (Physical AI), including humanoid robots, mobile manipulators, and other physically embodied intelligent systems. This study examines the applicability of the ODD concept to Physical AI. It identifies the dimensions required to develop a unified taxonomy for describing operational boundaries across heterogeneous physical AI platforms. The study adopts a conceptual comparative approach by analyzing the principal dimensions used to characterize automated-vehicle ODDs and comparing them with the operational characteristics and domain-related provisions in standards and frameworks for industrial and service robots. The analysis indicates that several ODD dimensions, including environmental conditions, operational areas, dynamic agents, infrastructure, and system capabilities, can be generalized to Physical AI. However, the broader operational variability of Physical AI requires additional dimensions addressing human–robot interaction, task context, workspace configuration, manipulation requirements, and platform mobility. Existing robotic standards contribute complementary concepts for safety, environmental constraints, human interaction, and operational conditions, but they do not provide a unified taxonomy applicable across different physical AI platforms. The findings support a layered taxonomy of the Physical AI domain that integrates environmental, infrastructural, task, interaction, mobility, and system-capability dimensions. Such a taxonomy can establish a common basis for defining, communicating, and evaluating operational boundaries while accommodating differences in platform configuration, intended tasks, and operating environments]]>
