A Comprehensive Review of Generative Physical Artificial Intelligence

2026-09-16

AI Summary

本文综述了生成式物理人工智能系统，通过五种方法如机器人基础模型和视觉-语言行动模型等解决复杂现实场景中的自主感知、推理和行动问题。

Abstract

The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics known as Generative Physical Artificial Intelligence (GPAI). These agentic AI systems autonomously perceive, reason, and act in complex real-world situations. This survey 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 multi-modal 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 VLAs and DPMs, RFMs enable cross-platform deployment of learned policies, while 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 IoT-connected environments where intelligent agents interact with networked sensors, actuators, and edge devices.

Problem

• Generative Physical Artificial Intelligence
• Robotics
• Foundation Models
• Autonomous Systems
• Perception and Control

Innovation

• Generative Physical Artificial Intelligence
• Robot Foundation Models
• Vision-Language Action models
• Large Behavior Models
• Diffusion Policy Models