Background
NVIDIA released a new foundational platform Cosmos, designed to power the next generation of physical AI—AI systems that perceive, reason, and act in the physical world. Introduced in 2025, Cosmos delivers a suite of large-scale World Foundation Models (WFMs) trained on over 20 million hours of multimodal data. These models offer a compelling foundation for building generalist robot policies—intelligent control systems that can perform a wide range of tasks across diverse environments.
They are trained on vast multimodal datasets that span vision, motion, language, and interaction dynamics, enabling them to understand and simulate complex physical processes. This makes WFMs have the potential to serve not only as the perceptual and predictive core of robotic systems, but as the cornerstone of truly general-purpose, real-world robot intelligence.
Sketch
As illustrated in the figure above, Cosmos receives an image of the initial setup along with a textual instruction, and outputs a sequence of actions—like joint angles and gripper state—that a robot arm can follow to complete the task.
Goal
This project aims to develop generalist robotic policies with the ability to understand and reason about the physical world. Central to this investigation is evaluating the feasibility of leveraging Cosmos as a backbone for these policies. By capitalizing on Cosmos’s large-scale, multimodal, and physics-aware representations, the project seeks to determine how well such models can generalize across diverse robotic tasks and environments.
The project also focuses on fine-tuning efficiency to examine how effectively we can adapt Cosmos to task-specific scenarios. This involves exploring the trade-offs between zero-shot generalization and lightweight task adaptation in both simulation and physical deployment settings.
Additionally, the project will investigate the viability of real-time inference, assessing whether Cosmos-based models can meet the latency and throughput requirements necessary for closed-loop control in real-world robotics.