mlsanityData to 7 Oct 2026

A Comprehensive Review of Generative Physical Artificial Intelligence

arXiv 2609.18111 ↗First posted 16 Sep 2026cs.RO cs.AI cs.CLcited by 0

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 80 of its 148 references that are in the map. First posted on arXiv 16 Sep 2026.

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80 of 148 references.

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  1. 2024Diffusion Transformer Policy21 Oct
  2. 2024The Ingredients for Robotic Diffusion Transformers14 Oct
  3. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusion7 Mar

Other references · 77

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