๐ก๐ฉ๐๐๐๐ ๐๐ผ๐๐บ๐ผ๐ ๐ฏ: ๐ ๐ก๐ฒ๐ ๐ช๐ฎ๐ ๐ณ๐ผ๐ฟ ๐ฃ๐ต๐๐๐ถ๐ฐ๐ฎ๐น ๐๐
Robot training used to be hard. You used many separate models. One for vision. One for planning. One for movement. These models made mistakes when they passed data.
NVIDIA Cosmos 3 fixes this. It is one model for everything. It handles reasoning and action together.
The system uses two towers.
The Reasoner tower understands the scene. It looks at images and video. It finds object positions and motion.
The Generator tower creates the output. It makes video or robot movements. It needs the reasoner's context to run.
Both towers share a 3D encoding system. This helps the model follow laws of physics. It knows about weight and friction.
There are three sizes:
- Nano: For workstations.
- Super: For datacenters.
- Edge: For cars and drones.
Use cases:
- Predict if a stack of blocks falls.
- Create synthetic training data.
- Generate robot motor commands.
Weights and code are on GitHub and Hugging Face.
There are limits. It needs a lot of power. Real-time speed is still hard.
It replaces messy pipelines with a clean base.
Source: https://dev.to/prabhakar_chaudhary_7afe4/nvidia-cosmos-3-unifying-physical-ai-reasoning-and-generation-with-two-tower-architecture-2j3f Optional learning community: https://t.me/GyaanSetuAi