WAMs and VLAs for Robot Learning | Cosmos Labs

Building capable robot foundation models requires more than one approach. Vision-language-action (VLA) models and World Action Models (WAMs) - each offer different strengths for robot learning, planning, and control. In this livestream, NVIDIA researchers Danfei Xu US Moritz Benno Reuss CH Thomas Tian US will explore what World Action Models work, how they compare with modern Vision-Language-Action (VLA) models, and why many next-generation robot foundation models combine both approaches. You'll also see how NVIDIA Cosmos 3 treats action as a native modality, enabling robots to predict future observations while generating actions. We'll walk through how Cosmos 3 provides an open workflow for post-training, evaluation, and deployment using open models, datasets, recipes, and serving resources. Whether you're building manipulation policies, training robot foundation models, or exploring embodied AI, this livestream provides a practical understanding of where World Action Models fit into the future of robotics. What You'll Learn: - What World Action Models are and how they differ from and complement Vision-Language-Action models - The strengths, limitations, and tradeoffs of WAMs, VLAs, and emerging hybrid approaches - How Cosmos3-Nano-Policy-DROID imagines future observations while generating robot actions - How NVIDIA Cosmos 3 represents action as a native modality alongside video - How to evaluate, post-train, and adapt Cosmos 3 using open models, datasets, recipes, and serving resources Have questions about how to post-train and deploy NVIDIA Cosmos 3? Drop them live — the NVIDIA team will answer them in real time. Access more NVIDIA Cosmos developer resources and join our developer community: 📄 Read the Technical Blog → https://nvda.ws/4c6kK3R ⬇️ Download Cosmos on Hugging Face → https://huggingface.co/collections/nvidia/cosmos3 📚 Explore Models & Datasets on GitHub → https://github.com/nvidia/Cosmos 👥 Join the Cosmos Community → https://discord.com/invite/nvidiaomniverse