Integrating ROS 2 and Physical AI: Architecture and Challenges

Abstract

Physical Artificial Intelligence (Physical AI) embeds sensors, actuators, and AI algorithms to enable robots to learn and adapt in real-world settings. ROS 2, with its DDS-based publisher-subscriber model and advanced Quality of Service (QoS) features, forms a robust platform for such real-time, distributed applications. Yet, integrating AI within a Cyber-Physical System (CPS) framework raises concerns about resource constraints, safety, and uncertainties. This paper explores how to incorporate Physical AI into ROS 2 by fully automating or partially augmenting robot functionalities, focusing on topic structures, message formats, and key QoS considerations. We highlight major challenges—data availability, data loss, inference latency, and security vulnerabilities—and emphasize the importance of a holistic approach to ensure reliability and safety.