Video2026
Tencent Hunyuan · China
A "living world" video model: it builds the world segment by segment, and you steer it with text and a camera trajectory. At each step you can add an object, change the style or scene, describe an action or turn the camera, and the memory of the world carries over between segments. Output is 720 by 1280 at 24 frames per second, and a segment takes 8 steps. The standard configuration is meant for several graphics cards, but there is a variant for a single RTX 4090 with INT8 weights and part of the model offloaded to the CPU. For narrow areas such as robotics a light LoRA tuning can be attached. Together with the model came the Hy-Nomas-Bench set of 280 test cases; the technical report and tests of the model itself are promised later.
- interactive video worlds and game-scene prototypes from a description
- test videos for robotics with LoRA tuning
- experiments with a controllable camera and scene consistency
- Sizes
- not stated (54 MMDiT blocks, a Qwen2.5-VL-7B text encoder)
- Hardware
- from: 1 GPU
3D2026
NVIDIA · USA
A diffusion model that creates three-dimensional body animation from a text description and set conditions: poses, positions of hands and feet, a route and points to pass through. There are versions for the SOMA skeleton (30 joints), the Unitree G1 robot (34 joints) and the SMPL-X human (22 joints). Version 1.2 is trained on a corrected set: in 1.1 some test recordings leaked into training, so the new version is the one to compare against other models. According to the authors, foot skating is reduced and text following is slightly weaker. The data of some variants is proprietary (Bones Rigplay), while the open variants are trained on Bones-SEED.
- animating characters for games and video from a description
- motions for humanoid robots (the Unitree G1 skeleton)
- motion drafts with a given route and poses
- Sizes
- not stated
- Hardware
- from: 1 GPU
Robotics2025–2026
NVIDIA · USA
"World" models for robots and self-driving vehicles: they generate realistic video of physical scenes and predict actions. Cosmos 3 combines understanding, generation and control. In October the surgical versions came out: Cosmos3-H-Embodied-Nano (16B, one model for predicting video, actions and policy on surgical robots) and Cosmos3-H-Dreams (a real-time world for simulating operations with the Versius robot). They are under the NVIDIA Open Model License.
- Synthetic video for training robots and self-driving vehicles
- Testing scenarios in simulation
- Robot control (Policy versions)
- Sizes
- 2B – 65B
- Hardware
- from: 1 GPU
Robotics2025–2026
GigaAI · China
A robot control model trained mostly on synthetic data from a world model. It reduces spending on collecting data from real robots.
- Controlling a robot arm
- Fine-tuning on a small amount of your own data
- Sorting and assembly pilots
- Sizes
- 3.5B
- Hardware
- from: 1 GPU
Robotics2026
Ant Group (Robbyant) · China
A robot control model from Ant Group trained on a large volume of data from real robots. Version 2.0 works with different types of robot arms.
- Controlling a two-armed robot
- Fine-tuning for your own operation
- Assembly and sorting pilots
- Sizes
- 4B – 6B
- Hardware
- from: 1 GPU
Robotics2026
Xiaomi · China
Open robot control models from Xiaomi. Robotics-1 is designed for household and kitchen tasks, U0 combines scene understanding and action.
- Controlling a robot arm by command
- Household and service scenarios
- Base for fine-tuning
- Sizes
- 4B – 5B
- Hardware
- from: 1 GPU
Robotics2025–2026
Ai2 (Allen Institute for AI) · USA
A fully open robot control model that first "reasons" about space and trajectory, then acts. Its reasoning can be checked.
- Controlling a robot arm with explainable steps
- Fine-tuning for your own robot
- Research pilots
- Sizes
- 5B – 8B
- Hardware
- from: 1 GPU
Robotics2025–2026
NVIDIA · USA
NVIDIA's foundation model for humanoid robots and robot arms: it sees, understands a command and outputs movements. Built into the Isaac ecosystem.
- Controlling humanoid robots
- Fine-tuning for your own robotic cell
- Training in simulation with transfer to a real robot
- Sizes
- 2B – 3B
- Hardware
- from: 1 GPU
Robotics2025
Physical Intelligence · USA
Robot control models from Physical Intelligence: folding laundry, tidying up, handling objects. π0.5 copes better in unfamiliar settings.
- Controlling robot arms and two-armed robots
- Fine-tuning for your own operations
- Pilots for automating manual work
- Sizes
- about 3B
- Hardware
- from: 1 GPU
Robotics2025
Hugging Face · USA
A small robot control model that runs on a regular laptop. Trained on open data from the LeRobot community, suited to low-cost robot arms.
- Controlling a low-cost robot arm
- Quick robotization pilots and demos
- Training staff and students
- Sizes
- 450M
- Hardware
- from: Laptop
Computer-use agentsGGUF2025
Microsoft Research · USA
An agent model that plans actions both in an interface (buttons on screen) and for a robot (arm movements). For now more of a research base than a finished product.
- Pilots in interface control
- Research projects spanning screens and robotics
- Analyzing screenshots with an action plan
- Sizes
- 8B
- Hardware
- from: 1 GPU
Robotics2024–2025
Stanford, Berkeley and partners · USA
The first large open vision-language-action model: a robot arm carries out commands like "put the apple in the bowl". OFT makes it several times faster.
- Controlling a robot arm by text command
- Pilots for robotizing simple operations
- Base for fine-tuning to your own robot
- Sizes
- 7B
- Hardware
- from: 1 GPU
