Beyond VLAs: How World Action Models Reshape Robot Manipulation | NVIDIA Technical Blog

This post details how World Action Models (WAMs), built on video world models, overcome the limitations of Vision-Language Models (VLMs) in robotics by learning world dynamics for better physical generalization and adaptation. It introduces the open NVIDIA Cosmos 3 model, a Mixture-of-Transformers (MoT) omni-model trained on a large multimodal dataset, as a strong foundation for WAMs. The post also describes post-trained policies like Cosmos3-Nano-Policy-DROID and Cosmos3-Edge-Policy-DROID, highlighting their ability to predict future world states alongside actions, retain the full omni architecture, and demonstrate measurable improvements in policy performance (e.g., RoboLab success rate increase from 28.1% to 36.8% with an omni checkpoint). Deployment considerations for workstation serving and on-device inference are also discussed.