A Comprehensive Review of Generative Physical Artificial Intelligence

Abstract

The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics termed generative physical artificial intelligence (GPAI). These agentic AI systems autonomously perceive, reason, and act in complex real-world situations. This article comprehensively analyzes GPAI systems, focusing on their architectural foundations, current applications, and key limitations. We introduce a taxonomy of five distinct approaches: robot foundation models (RFMs) for cross-platform skill transfer; vision–language–action (VLA) models for end-to-end multimodal perception and control; large behavior models (LBMs) for human-like movement generation; diffusion policy models (DPMs) for diffusion model-based temporally coherent action generation; and world foundation models (WFMs) for physics-compliant simulation and data generation. We examine how these approaches complement each other: WFMs generate training data for VLA models and DPMs, RFMs enable cross-platform deployment of learned policies, and LBMs provide motion priors for natural behavior. Through examples across autonomous vehicles, industrial automation, healthcare robotics, and humanoid systems, we identify significant performance improvements and summarize promising research directions in data-efficient learning, sim-to-real transfer, edge-compatible architectures, and safety frameworks. These insights advance embodied AI for the Internet of Things (IoT)-connected environments, where intelligent agents interact with networked sensors, actuators, and edge devices.